Authorship：Lead author   Language：Japanese   Publishing type：Research paper (scientific journal)  

CiNii Research

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Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/eusipco.2015.7362870

CiNii Research

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Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/icics.2013.6782856

CiNii Research

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Scientific Research Publishing, Inc.  

DOI： 10.4236/cs.2013.41017

CiNii Research

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Other Link： http://www.scirp.org/journal/doi.aspx?DOI=10.4236/cs.2013.41017

Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Institute of Electronics, Information and Communications Engineers (IEICE)  

DOI： 10.1587/transfun.e95.a.1310

CiNii Research

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Institute of Electronics, Information and Communications Engineers (IEICE)  

DOI： 10.1587/transfun.e95.a.2046

CiNii Research

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Authorship：Lead author   Language：Japanese   Publishing type：Research paper (scientific journal)  

CiNii Research

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Other Link： https://ndlsearch.ndl.go.jp/books/R000000004-I10960796

Publishing type：Research paper (scientific journal)   Publisher：Research Institute of Signal Processing, Japan  

DOI： 10.2299/jsp.30.41

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Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.23919/icact68090.2026.11431301

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Springer Science and Business Media LLC  

DOI： 10.1007/s10044-025-01578-8

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Other Link： https://link.springer.com/article/10.1007/s10044-025-01578-8/fulltext.html

Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/incit66780.2025.11276104

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Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/incit66780.2025.11276007

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Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/apsipaasc65261.2025.11249343

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Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/apsipaasc65261.2025.11249415

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Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.23919/eusipco63237.2025.11226456

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Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/isas66241.2025.11101893

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Language：English   Publishing type：Research paper (scientific journal)  

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Publishing type：Part of collection (book)   Publisher：Springer Nature Singapore  

DOI： 10.1007/978-981-96-1535-3_33

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Publishing type：Part of collection (book)   Publisher：Springer Nature Singapore  

DOI： 10.1007/978-981-96-1535-3_31

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Publishing type：Part of collection (book)   Publisher：Springer Nature Singapore  

DOI： 10.1007/978-981-96-1535-3_32

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Publishing type：Part of collection (book)   Publisher：Springer Nature Singapore  

DOI： 10.1007/978-981-96-1535-3_34

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Publishing type：Research paper (scientific journal)   Publisher：Wiley  

Abstract

Cognitive radio is an intelligent technology for wireless communication that optimizes the use of available frequency bands. Machine learning techniques can play an important role in spectrum sensing for cognitive radio networks to meet the rising traffic demand of wireless communication systems. The reliability of spectrum sensing methods depends on the prior knowledge of the noise to set a threshold. On the other hand, the success of a machine learning model relies on both the datasets and the accuracy of its learning algorithms. In this paper, we propose a spectrum sensing method for cognitive radio based on a machine learning algorithm in the conventional energy detection technique that removes the requirement to calculate the threshold. Initially, we introduce a method to build the dataset using the general concept of spectrum sensing based on the energy detection technique. The Naive Bayes supervised machine learning classification algorithm is implemented on the generated dataset for training, validation, and testing to sense the available spectrum. The proposed method is evaluated and tested using performance metrics such as confusion matrix, accuracy, precision, recall, F1 score, probability of detection, and probability of false alarm. In the simulation, the quadrature phase‐shift keying (QPSK) modulation scheme over the additive white Gaussian noise (AWGN) channel is considered. The experimental outcomes of the proposed method provide satisfactory and acceptable performance for spectrum sensing in cognitive radio networks. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

DOI： 10.1002/tee.24261

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Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/ispacs62486.2024.10868890

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Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/ispacs62486.2024.10868228

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Publishing type：Research paper (scientific journal)   Publisher：Science Publishing Group  

<p lang="en">Cognitive radio (CR) is one of the compelling ideas to solve the spectrum scarcity problem for rapid developments in wireless communication systems. In CR systems, signal detection for orthogonal frequency division multiplexing (OFDM) systems in severe noise environments is a key challenge. The area of practical primary user detection has not been explored in depth. The proposed method is an effective method for sensing OFDM applications, which are the practical primary users, for low signal-to-noise (SNR) cases. In the proposed method, the parallel combination of the comb filter and the time-domain autocorrelation function is exploited. The detection performance is measured for various OFDM system applications, including the IEEE 802.11a wireless LAN (WLAN) radio interface, long-term evaluation (LTE), and digital audio broadcasting (DAB) for various CP ratios under 16-quadrature amplitude modulation (16-QAM) and 64-quadrature amplitude modulation (64-QAM) over multipath Rayleigh fading channels with additive white Gaussian noise (AWGN). Furthermore, the OFDM sensing is possible in the presence of noise uncertainty and the sensing performance is compared under consideration with and without noise uncertainty cases. The simulation results demonstrated that our proposed method undoubtedly improves the sensing performances (up to 11 dB SNR gain) of practical primary users more than the conventional spectrum detection methods for low SNR cases.</p>

DOI： 10.11648/j.ajnc.20241302.12

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Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/icip51287.2024.10647440

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Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.23919/eusipco63174.2024.10715075

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Publishing type：Research paper (scientific journal)   Publisher：Research Institute of Signal Processing, Japan  

DOI： 10.2299/jsp.28.119

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Publishing type：Research paper (scientific journal)   Publisher：Wiley  

Abstract

This paper introduces EmoBone, a comprehensive audio‐only emotional bone‐conducted speech dataset featuring speakers from various countries. The dataset comprises speeches from 28 individuals representing 10 different nations, with each participant delivering 10 sentences designed to evoke distinct emotions. In addition to an air‐conducted microphone, the recordings utilized bone conduction technology, transmitting sound directly to the speakers' inner ears, ensuring high‐quality emotional speech recordings. To assess the validity of the dataset, 80 university students from Bangladesh listened to the recordings and successfully identified the expressed emotions with an accuracy exceeding 76%. Statistical methods were also employed to evaluate the reliability of the dataset, revealing a high level of agreement among raters. EmoBone, with a cumulative duration surpassing 19 h and 15 680 unique utterances, stands as the most extensive emotional speech dataset available. This makes it a valuable tool for studying how emotional speech varies across cultures. Furthermore, due to its utilization of bone conduction technology, EmoBone facilitates the study of acoustic features in emotional speech from diverse dimensions. The data that supports the findings of this study is available upon reasonable request. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

DOI： 10.1002/tee.24110

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Publishing type：Research paper (scientific journal)   Publisher：Research Institute of Signal Processing, Japan  

DOI： 10.2299/jsp.28.77

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Publishing type：Research paper (scientific journal)   Publisher：The Science and Information Organization  

DOI： 10.14569/ijacsa.2024.01504126

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Publisher：ACM  

DOI： 10.1145/3655755.3655774

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Research Institute of Signal Processing, Japan  

We propose a blind image quality assessment (BIQA) method of using the multitask-learning-based end-to-end convolutional neural network (CNN) approach. The architecture of the proposed method is integrated by two streams. In the first stream, multiscale image features are extracted by using the inception and pyramid pooling modules. Natural scene statistics (NSS)-based features are extracted in the second stream. The two streams are then integrated into fully connected layers to estimate the image quality score. The performance of the proposed method is validated with four public IQA databases and the obtained experimental results show the superiority of the proposed method over conventional IQA methods.

DOI： 10.2299/jsp.28.45

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Research Institute of Signal Processing, Japan  

In this paper, we propose the incorporation of noisy image patches and the impulse response of a low-pass filter (LPF) in a convolutional neural network (CNN) to denoise Poisson–Gaussian noise in low-dose computed tomography (LDCT) images. The approach is referred to as fast and flexible denoising CNN (FFDNet)-impulse response (FFDNet-IR) in this paper. The power spectrum sparsity LPF (SLPF) allows low-frequency components to pass through while suppressing higher frequency components by the sparsity approach of the power spectrum, and it is employed to determine the impulse response of LPF. Three well-known types of LPF, namely, Direct LPF, Gaussian LPF, and Butterworth LPF, are also considered to obtain the impulse response of LPF. In the FFDNet-IR, both the noisy image patches and the IR of the LPF are sequentially inputted into the FFDNet to eliminate the Poisson–Gaussian noise. This approach enhances the denoising performance in LDCT images compared with the conventional FFDNet in the evaluation metrics of the peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and feature similarity (FSIM). Moreover, the FFDNet-IR trained with the Poisson–Gaussian noise model demonstrates the generalization ability and effectively eliminates only Poisson or Gaussian noise. The experiments indicate that the FFDNet-IR more effectively suppresses the noise artifacts and preserves image details compared with the baseline FFDNet, as well as traditional methods such as block-matching and 3D filtering (BM3D) and nonlocal mean (NLM) for LDCT image denoising.

DOI： 10.2299/jsp.28.57

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Research Institute of Signal Processing, Japan  

In this paper, we propose the sequential input of a noisy image patch and the impulse response of a low-pass filter (LPF) in the training of the conventional fast and flexible solution for CNN-based image denoising (FFDNet) architecture, which enhances denoising performance and edge preservation and achieves high perceptual quality. The proposed method consists of two steps. In the first step, the power spectrum sparsity is utilized to determine the impulse response of LPF and the resulting impulse response is added to the noisy image patch in a sequential form to estimate the low- and high-frequency components of the input image. In this step, the use of three different types of LPF is also considered. In the second step, the FFDNet architecture, a deep-learning-based image denoiser, is employed. The proposed method achieves satisfactory denoising performance for grayscale and color datasets on synthetic additive white Gaussian noise (AWGN) in terms of the peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), feature similarity index (FSIM), and learned perceptual image patch similarity (LPIPS) compared with the original FFDNet. The performances on realistic noise and for chest X-ray images are also investigated.

DOI： 10.2299/jsp.28.1

DOI： 10.2299/jsp.28.57_references_DOI_TBPYhwBzSB59IXoibpFrP1Wsd3f

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Research Institute of Signal Processing, Japan  

In this paper, we propose a blind noisy image quality estimation method of simultaneously utilizing three statistical features extracted from three different domains of the input noisy image. The statistical features used in this paper are (i) eigen-based variance by the covariance matrix of image blocks in the spatial domain, (ii) the spectral entropy of the power spectrum in the frequency domain, and the standard deviation in the wavelet domain. The extracted statistical features are fed into an extreme learning machine algorithm for mapping into perceptual quality scores. The model is trained and tested on images with six common noise distortion types commonly occurring in real-world applications: additive white Gaussian noise, additive Gaussian noise in color component, high-frequency noise, masked noise, impulse noise, and multiplicative noise. For the CSIQ, TID2008, TID2013, and KADID10k databases, the experimental results show that our method covers noise distortions wider than those of the conventional methods and achieves consistently better performance for blind noisy image quality assessment.

DOI： 10.2299/jsp.28.19

CiNii Research

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Publishing type：Research paper (international conference proceedings)  

DOI： 10.1145/3719384.3719416

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Other Link： https://dblp.uni-trier.de/db/conf/aiccc/aiccc2024.html#TunSS24

Publishing type：Research paper (international conference proceedings)  

DOI： 10.1145/3723178.3723225

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Other Link： https://dblp.uni-trier.de/db/conf/icca2/icca2024.html#HosainSRS24

Language：English   Publishing type：Research paper (scientific journal)   Publisher：Research Institute of Signal Processing, Japan  

Speech emotion recognition has drawn extensive attention in recent years. We propose deep learning (DL)-based speech emotion recognition using synthetic bone-conducted (BC) speech. In our proposed model, air-conducted(AC) speech is transformed to BC speech using an infinite impulse response (IIR) filter. Data augmentation techniques are utilized and the parameters of convolutional neural network (CNN) models are modified to enhance the accuracy of the proposed model. Simulation results demonstrate that the proposed model outperforms the existing models in terms of recognition accuracy for BC speech. The accuracy of the proposed model is 72.50% for BC speech, whereas that of the existing model is 69.83% for AC speech. This is because BC speech can enhance low-frequency components, which is important for recognizing emotions.

DOI： 10.2299/jsp.27.151

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/gcce59613.2023.10315451

DOI： 10.2299/jsp.28.57_references_DOI_KarGpK2lTjlbqin5IyHbHLAPiZj

CiNii Research

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/gcce59613.2023.10315629

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Wiley  

<jats:p>In this paper, we compare air‐conducted (AC) speech with bone‐conducted (BC) speech for the purpose of utilizing them in packet loss concealment (PLC) for speech quality of voice over internet protocol (VoIP). Instead of the autocorrelation method of linear prediction (LP), which was utilized in the conventional PLC techniques, we employ the modified covariance (MC) method. The MC method provides accurate LP estimation from short input data samples and avoids the numerical problem the autocorrelation method suffers from. The lost frame is compensated from both forward and backward directions in which linear gain and weighting are applied. When BC speech is used as the input speech data for PLC in the case where the speech sender is in noisy environments, BC speech behaves more accurately than the corresponding AC speech, resulting in an excellent performance of the LP‐based PLC technique. This unveils a useful use of BC speech in speech information systems. Experiments show that in severe noise environments of AC speech‐to‐noise ratio being less than 10 dB, BC speech is superior to AC speech for PLC. It is also shown that the transmitted BC speech is more accurately reconstructed for PLC than transmitted AC speech is done. © 2023 The Authors. <jats:italic>IEEJ Transactions on Electrical and Electronic Engineering</jats:italic> published by Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.</jats:p>

DOI： 10.1002/tee.23907

DOI： 10.2299/jsp.28.77_references_DOI_ZLg9atd3CNBos0qGhAgMRnRywcZ

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Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/SENSORS56945.2023.10325095

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Other Link： https://dblp.uni-trier.de/db/conf/ieeesensors/ieeesensors2023.html#ChiSS23

Language：English   Publishing type：Research paper (scientific journal)   Publisher：Research Institute of Signal Processing, Japan  

In this paper, we propose a two-stage network for real image denoising with filter response normalization, named as two-stage filter response normalization network (TFRNet). In TFRNet, we propose a filter response normalization(FRN) block to extract features and accelerate the training of the network. TFRNet consists of two stages, at each stage of which we use the encoder-decoder structure based on U-Net. We also use the coordinate attention block(CAB), double channel downsampling module, double skip connection module, and convolutional (Conv) block in our TFRNet. With the help of these modules, TFRNet provides excellent results on both SIDD and DND datasets for real image denoising.

DOI： 10.2299/jsp.26.183

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Wiley  

<jats:p>In this paper, we present a noise‐robust pitch extraction method in which air‐conducted (AC) speech and bone‐conducted (BC) speech are utilized simultaneously as the input signals. Due to noise independency in both the input signals and noise suppression effect in BC speech, peak characteristics created in different functions are significantly enhanced so that accurate pitch extraction is achieved even in highly noisy environments. Experimental results show a superior performance of the proposed method relative to the state‐of‐the art method in several types of noises. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.</jats:p>

DOI： 10.1002/tee.23596

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Wiley  

<jats:p>In this paper, we present a methodology to synthesize bone‐conduced (BC) speech. Without relying on commonly used techniques for speech synthesis, we consider to transform air‐conducted (AC) speech into BC speech. An infinite impulse response (IIR) filter is explored for the transformation. From the concept of system identification, the least squares (LS) method is employed and the IIR filter is designed through the recorded AC and BC speech data. By experiments, it is shown that the BC speech synthesis method is satisfactory. Filter model, order selection and stability in the methodology are discussed and noise‐robustness gained by the BC speech synthesis is also investigated. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.</jats:p>

DOI： 10.1002/tee.23531

DOI： 10.2299/jsp.27.151_references_DOI_BxVwCaPUkwHkUOoyr6mmTFkySWM

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Other Link： https://onlinelibrary.wiley.com/doi/full-xml/10.1002/tee.23531

Language：English   Publishing type：Research paper (scientific journal)   Publisher：Research Institute of Signal Processing, Japan  

In this paper, we present the use of the fourth-root spectrum instead of the log spectrum for pitch extraction in noisy environments. To obtain clear harmonics, lifter and clipping operations are performed. When the resulting spectrum is transformed into the time domain by the discrete Fourier transform, pitch detection is robust against narrow-band noise. When the same spectrum is amplified by power calculation and transformed into the time domain, pitch detection is robust against wide-band noise. These properties are investigated through exhaustive experiments in various noises. The required computational time is also studied.

DOI： 10.2299/jsp.24.207

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Wiley  

<jats:title>Abstract</jats:title><jats:p>In this paper, considering a progressing trend of recent techniques for pitch detection of speech in noisy environments, windowing effects are discussed analytically, and it is insisted that the Rectangular window should be proactively used instead of the popular Hanning or Hamming window. In a variety of noise environments, a performance comparison of the conventional pitch detection methods is conducted, and as a result, we take a standpoint to support the autocorrelation (ACF) method. Incorporating accumulation techniques, three types of pitch detection approaches are developed. Through experiments, it is shown that the three accumulation based approaches have the potential to provide better performance than recent state‐of‐the art methods for pitch detection without relying on a complicated post processing technique. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.</jats:p>

DOI： 10.1002/tee.23238

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Research Institute of Signal Processing, Japan  

In this paper, we propose a new training architecture for speech enhancement based on deep neural networks. In the proposed architecture, the generative model producing the noiseless speech is trained so as to minimize the difference between two statistical distribution parameters of the clean speech and generated speech. From the experimental results, we verify that the proposed method can provide better results than the conventional method.

DOI： 10.2299/jsp.24.179

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/MWSCAS48704.2020.9184700

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Other Link： https://dblp.uni-trier.de/db/conf/mwscas/mwscas2020.html#ZhangSYS20

Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/ispacs48206.2019.8986375

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Research Institute of Signal Processing, Japan  

Blind image quality assessment (BIQA) methods can measure the quality of distorted images even without referencing the original images. This property is indispensable in the image processing field because reference images are normally not available in practice. Unlike the existing trained models, in our work, the training process is constructed as an end-to-end learning mechanism that minimizes the loss between the predicted score and the ground-truth score of the human vision system (HVS). Moreover, a convolutional neural network (CNN) takes distorted images as input and outputs the related score for each image. In this paper, we evaluate the proposed method on six publicly available benchmarks and the cross-database validation performance on the LIVE, CSIQ and TID2013 databases. The experimental results show that our proposed method outperforms other state-of-the-art methods.

DOI： 10.2299/jsp.23.267

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Research Institute of Signal Processing, Japan  

In this paper, we propose a new combination technique for full-reference image quality assessment (IQA) by utilizing three better-recognized IQA methods. To select the IQA methods, we first pick up Most Apparent Distortion (MAD) as the most appropriate IQA index for image quality databases and then add two other indices, MS-SSIM and FSIM, which have the most dissimilar features from the first index MAD. The parameter values employed in the new IQA score are optimized using the particle swarm optimization algorithm. By experiments, it is validated that the proposed method gives the best performance for various databases and outperforms the other state-of-the-art methods.

DOI： 10.2299/jsp.23.205

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Research Institute of Signal Processing, Japan  

In this paper, we consider a single-input multiple-output (SIMO) channel-based static wireless sensor network and carry out blind equalization to estimate the transmitted signal blindly. Four cases of common or different channels and a common or different variance of noises are considered. For each case, the solution of blind equalization is derived. For the different-channel cases, we derive a new approach in which the best sensor output signal is found by adaptively implementing the normalized error used in speech processing. We estimate the transmitted signal from the corresponding sensor output by utilizing the generalized Sato equalizer. The mean square error (MSE) and symbol error rate (SER) are investigated on several communication channels. Computer simulations validate the solution for each case and show the effectiveness of the proposed method relative to the conventional methods.

DOI： 10.2299/jsp.23.215

DOI： 10.2299/jsp.27.35_references_DOI_QPWp1fLEgrswJbWJphb47ChtHP0

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：EJournal Publishing  

DOI： 10.18178/ijfcc.2019.8.3.547

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Publishing type：Research paper (international conference proceedings)  

DOI： 10.1109/icicsp48821.2019.8958528

Web of Science

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Research Institute of Signal Processing, Japan  

A novel method for edge component detection based on image power spectrum sparsity is presented. The edge size can be varied by changing the block size and threshold parameter to obtain the desired edge component. The image is first divided into sub-blocks and the power spectrum sparsity for each sub-block is calculated. On the basis of the image power spectrum sparsity value, each block is verified by the threshold value to determine the edge component. The experimental results show that the proposed method is suitable for object tracking because of the novel feature of the flexible edge size, which can dramatically reduce the amount of data to be stored.

DOI： 10.2299/jsp.23.189

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Research Institute of Signal Processing, Japan  

Noise level estimation is important to improve the performance of different image-processing algorithms. Among the different noise level estimation methods, a block-based approach is one of the most effective approaches for estimating the noise level. A noise level estimation method based on a weak-texture patch using the image power spectrum sparsity in the frequency domain is proposed in this paper. A weaktexture image patch is first selected according to the value of image power spectrum sparsity. From the selected weak-texture image patch, the noise variance is estimated by selecting the frequency regions where the image frequency parts are not concentrated. It is observed that the proposed noise level estimation method is effective, especially for images with a rich texture. Furthermore, the proposed method provides a shorter computational time than the conventional methods.

DOI： 10.2299/jsp.23.95

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Research Institute of Signal Processing, Japan  

A simple and effective denoising method for a spectral subtractive (SS)-type parametric Wiener filter (PWF) for a blind condition is proposed. A simple noise estimation method is used to estimate the noise variance directly from a noisy image. Preliminary experiments with trained images are conducted to find the best parameters for the PWF. The PWF gives the highest performance with the best parameter setting. However, in practice, it is difficult to know the best parameters because they depend on the characteristics of the image. To estimate the best parameters for the PWF, therefore, a novel tool named image power spectrum sparsity, which is not influenced by the noise level, is derived. The parameters for the PWF are set according to the power spectrum sparsity. To demonstrate the effectiveness of the PWF, untrained images are used. The experimental results show that the proposed method gives a good performance with the shortest computational time among the WF methods to restore an image under a blind condition.

DOI： 10.2299/jsp.22.287

DOI： 10.2299/jsp.28.57_references_DOI_MfeYpKG9mYztieDZ0ACE9nANYuR

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/tencon.2018.8650288

DOI： 10.2299/jsp.23.243_references_DOI_XYEaFBMih3grP4xFa0m0POHyVOA

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Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/ispacs.2017.8266475

CiNii Research

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/iciev.2017.8338580

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/ecace.2017.7912912

CiNii Research

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/apccas.2016.7803881

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/iscas.2015.7169117

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：The Institute of Image Information and Television Engineers  

DOI： 10.3169/itej.68.j385

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Institute of Electronics, Information and Communications Engineers (IEICE)  

DOI： 10.1587/transfun.e97.a.2611

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Scientific Research Publishing, Inc.  

DOI： 10.4236/cs.2013.41002

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Other Link： http://www.scirp.org/journal/doi.aspx?DOI=10.4236/cs.2013.41002

J-GLOBAL

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J-GLOBAL

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J-GLOBAL

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J-GLOBAL

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J-GLOBAL

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J-GLOBAL

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Language：Japanese   Publisher：[福岡] : 日本音響学会  

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Other Link： https://ndlsearch.ndl.go.jp/books/R000000004-I033975996

Language：Japanese   Publisher：東京 : 電気学会  

CiNii Research

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Other Link： https://ndlsearch.ndl.go.jp/books/R000000004-I033514255

J-GLOBAL

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Authorship：Lead author  

J-GLOBAL

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J-GLOBAL

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