BM3D image denoising algorithm based on an adaptive filtering

Yahya, Ali Abdullah; Tan, Jieqing; Su, Benyue; Hu, Min; Wang, Yibin; Liu, Kui; Hadi, Ali Naser

doi:10.1007/s11042-020-08815-8

Cited by 49 publications

(9 citation statements)

References 35 publications

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“…In addition, the following are some of the latest methods added to the comparative experiment. In order to mitigate the above-stated drawbacks of the BM3D filter, Yahya et al [89] proposed to replace the steady hard-thresholding of BM3D by the adaptive filtering (i.e., soft-thresholding and total variation) function. In the proposed adaptive filtering function, soft-thresholding is applied to the high-noise areas, in contrast, the total variation is applied to the light-noise areas.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

A Novel Gray Image Denoising Method Using Convolutional Neural Network

Meng

Zhang

2022

IEEE Access

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In order to make the image denoising more effective in high noise level environment, a gray image denoising method based on symmetric dilation residual network is proposed for additive Gaussian white noise. First, the incremental & dilated convolution, BN (Batch Normalization) and leaky ReLU (Rectified Linear Unit) are used to extract and learn features from the input noise image. Then the image is reconstructed by combining the descending & dilated convolution and ReLU. Finally, the effective separation between image and noise is realized by integrating deep residual network and BN. Experimental data of specific denoising model and stochastic denoising model show that this method achieves better objective results based on small resolution input & large resolution input and small dataset & large dataset compared with other methods. A series of subjective visual comparison results also show that proposed method can perform image denoising well in high noise level environment, and there are no problems such as damaged edge or texture details, boundary artifacts and poor definition of the denoised image. The proposed method not only improves the image denoising ability, but also improves the denoising productivity to some extent. In the related work, we have made a comprehensive discussion on the current mainstream image denoising methods based on traditional machine learning and deep learning, which to some extent makes up for the lack of such reviews. In addition, the existing problems and future development direction of image denoising are discussed in detail.

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Section: Experimental Results and Analysismentioning

confidence: 99%

A Novel Gray Image Denoising Method Using Convolutional Neural Network

Meng

Zhang

2022

IEEE Access

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“…The impulsive noise is also affecting the efficiency of the Block-Matching and 3D Filtering, (BM3D) 17 , 18 which is exploiting the image local sparse representation in the transform domain 19 – 23 and dampen the Gaussian noise operating on a 3D stack of the local patches from the sliding filtering block, applying a collaborative filtering-based shrinkage strategy. The Anisotropic Diffusion filter 24 also cannot handle the impulses, as the local gradients between an impulsive pixel and its neighbors are high, which remarkably slows down the diffusion process and preserves the outlying pixels 25 .…”

Section: Related Workmentioning

confidence: 99%

Robust mean shift filter for mixed Gaussian and impulsive noise reduction in color digital images

Kusnik

Smołka

2022

Sci Rep

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Noise reduction is one of the most important topics of digital image processing and despite the fact that it has been studied for a long time it remains the subject of active research. In the following work, we present an extension of the Mean Shift technique, which is efficiently reducing the Gaussian noise, so that it is able to cope with the impulsive disturbances. Furthermore, the elaborated technique can be applied to enhance the images corrupted by a mixture of strong Gaussian and impulsive noise, severely decreasing the quality of color digital images. By means of our approach, which is based on a novel similarity measure between a pixel and a patch located in the center of the processing block, even heavily disturbed images can be effectively restored, which enables the success of further stages of the image processing pipeline. We evaluate the efficiency of the proposed method using a publicly available database of test color images and compare the restored images applying a set of standard quality metrics with the results delivered by state-of-the-art denoising methods. Additionally, we compare our method with the Medoid and Quick Shift techniques, accelerating the original Mean Shift algorithm, in terms of objective quality criteria and computational complexity. The results of the performed experiments indicate that the proposed technique is superior to the widely used denoising techniques and can be used as a robust extension of the Mean Shift procedure. In the paper, a particular emphasis is placed on the ability of the presented algorithm to preserve and enhance image edges. The performed experiments evaluated with the use of the Pratt’s index, quantitatively confirm the superiority of the proposed design over the Mean Shift and standard denoising methods. The preservation of edges and even their sharpening is a very important feature of our algorithm whereas the final goal is segmentation, playing a crucial role in various computer vision tasks. The proposed algorithm is intended for the mixed noise reduction in color images, but it can be also applied in multispectral imaging and clustering of multidimensional data. To enable the comparison of our method with the standard denoising techniques and to help applying it in other image processing fields, we made its code freely available.

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“…Finally, transformdomain collaborative filtering is applied to obtain the clean patch. Yahya et al (2020) used adaptive filtering to improve BM3D. WNNM Gu et al (2014) performs denoisng by applying low rank matrix approximation to the stacked noisy patches.…”

Section: Image Denoisingmentioning

confidence: 99%

Joint image compression and denoising via latent-space scalability

Alvar

Ulhaq

Choi

et al. 2022

Front. Signal Process.

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When it comes to image compression in digital cameras, denoising is traditionally performed prior to compression. However, there are applications where image noise may be necessary to demonstrate the trustworthiness of the image, such as court evidence and image forensics. This means that noise itself needs to be coded, in addition to the clean image itself. In this paper, we present a learning-based image compression framework where image denoising and compression are performed jointly. The latent space of the image codec is organized in a scalable manner such that the clean image can be decoded from a subset of the latent space (the base layer), while the noisy image is decoded from the full latent space at a higher rate. Using a subset of the latent space for the denoised image allows denoising to be carried out at a lower rate. Besides providing a scalable representation of the noisy input image, performing denoising jointly with compression makes intuitive sense because noise is hard to compress; hence, compressibility is one of the criteria that may help distinguish noise from the signal. The proposed codec is compared against established compression and denoising benchmarks, and the experiments reveal considerable bitrate savings compared to a cascade combination of a state-of-the-art codec and a state-of-the-art denoiser.

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BM3D image denoising algorithm based on an adaptive filtering

Cited by 49 publications

References 35 publications

A Novel Gray Image Denoising Method Using Convolutional Neural Network

A Novel Gray Image Denoising Method Using Convolutional Neural Network

Robust mean shift filter for mixed Gaussian and impulsive noise reduction in color digital images

Joint image compression and denoising via latent-space scalability

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