Design of Natural Image Denoising Filter Based on Second-Generation Wavelet Transformation and Principle Component Analysis

Khmag, Asem; Ramli, Abd Rahman; Al-Haddad, S. A. R.; Hashim, S. J.; Noh, Zarina Mohd; Najih, Abdulmawla A. M.

doi:10.1166/jmihi.2015.1523

Cited by 16 publications

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“…All signal de-noising effect highlights a complete dictionary of sparse decomposition method is significantly superior to complete dictionary of orthogonal decomposition denoising performance, fully illustrates the redundant dictionary than orthogonal dictionary is more accurate and adaptive representation of the original signal, and the absolute advantage of sparse decomposition de-noising method was verified [30][31][32][33][34]. Independent threshold value method is according to different needs, choose different from the default threshold value, then use reconstruction algorithm for de-noising reconstruction.…”

Section: The Proposed Novel De-noising Algorithmmentioning

confidence: 90%

A Novel Audio Signal De-noising Algorithm based on the Theory of Frequency Domain Analysis and Transform Domain Model

Ma¹

2017

IJFGCN

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Section: The Proposed Novel De-noising Algorithmmentioning

confidence: 90%

A Novel Audio Signal De-noising Algorithm based on the Theory of Frequency Domain Analysis and Transform Domain Model

Ma¹

2017

IJFGCN

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“…The sub-bands labeled as LH 1 , HL 1 , and HH 1 are sets of wavelet coefficients in the finest level. The following coarser level of sub-bands, namely, LH 2 , HL 2 , and HH 2 , are derived from the scaling coefficients in the finer level LL 1 [36]. The subsequent decomposition levels are applied until a certain level of decomposition is achieved.…”

Section: Second-generation Wavelet Transformmentioning

confidence: 99%

Denoising of natural images through robust wavelet thresholding and genetic programming

et al. 2016

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Digital images play an essential role in analysis tasks that can be applied in various knowledge domains, including medicine, meteorology, geology, and biology. Such images can be degraded by noise during the process of acquisition, transmission, storage, or compression. The use of local filters in image restoration may generate artifacts when these filters are not well adapted to the image content as a result of the heuristic optimization of local filters. Denoising methods based on learning procedure are more capable than parametric filters for addressing the conflicts between noise suppression and artifact reduction. In this study, we present a nonlinear filtering method based on a two-step switching scheme to remove both salt-and-pepper and additive white Gaussian noises. In the switching scheme, two cascaded detectors are used to detect noise, and two corresponding estimators are employed to effectively and efficiently filter the noise in an image. In the process of training, a method according to patch clustering is utilized, and genetic programming (GP) is subsequently applied to determine the optimum filter (wavelet-domain filter) for each individual cluster, while B Asem Khmag in testing part, the optimum filter trained beforehand by GP is recovered and used on the inputted corrupted patch. This adaptive structure is employed to cope with several noise types. Experimental and comparative analysis results show that the denoising performance of the proposed method is superior to that of existing denoising methods as per both quantitative and qualitative assessments.

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“…However, DWT has the disadvantage of removing major parts of the main digital picture. Despite these disadvantages, the wavelet-based denoising methods, especially the secondgeneration wavelet-based denoising, outperform due to the high efficiency of the reconstruction process and the link to real wavelets feature [19], [20]. To supplement this, a deep learning-based denoising technique that learns and restores information from images has been proposed [21], [22].…”

Section: Introductionmentioning

confidence: 99%

Multilevel Feature Extraction Using Wavelet Attention for Deep Joint Demosaicking and Denoising

2022

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Demosaicking and denoising are critical for deciding on digital camera performance. However, conventional joint demosaicking and denoising methods use traditional hand-crafted filters for preprocessing and image restoration. Therefore, it is susceptible to noise and can produce many artifacts for images with numerous edges. This paper proposes an end-to-end multi-level wavelet attention convolutional neural network (CNN) that improves image restoration performance by reducing false color artifacts, over-smoothing, and blurring during demosaicing and denoising. In detail, this paper proposes a CNN-based preprocessing method that learns the mosaic image's inter-color correlation, improving the edge's color restoration performance. Furthermore, this paper presents a multi-level feature extraction convolutional block using the Haar wavelet-based discrete wavelet transform (DWT) to remove noise from images and restore colors better at the same time. Therefore, it preserves the information of input image and feature maps, learns the correlation between global and local features, improves image restoration performance, and suppresses phenomena such as over-smoothing that tend to occur in DWT-based denoising. The proposed method is an end-to-end network structure with CNN-based preprocessing methods. In experimental results, the proposed method improves PSNR by 3.67 dB and 0.35 dB on average compared with the traditional methods and the CNN-based methods, respectively, for the dataset with many high-frequency components.

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Design of Natural Image Denoising Filter Based on Second-Generation Wavelet Transformation and Principle Component Analysis

Cited by 16 publications

References 0 publications

A Novel Audio Signal De-noising Algorithm based on the Theory of Frequency Domain Analysis and Transform Domain Model

A Novel Audio Signal De-noising Algorithm based on the Theory of Frequency Domain Analysis and Transform Domain Model

Denoising of natural images through robust wavelet thresholding and genetic programming

Multilevel Feature Extraction Using Wavelet Attention for Deep Joint Demosaicking and Denoising

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