Blind-noise image denoising with block-matching domain transformation filtering and improved guided filtering

Jia, Hongbin; Yin, Qinye; Lu, Mingyu

doi:10.1038/s41598-022-20578-w

Cited by 7 publications

(5 citation statements)

References 37 publications

(33 reference statements)

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“…The powerful Non-local Means (NLM) technique 46 , 47 utilizes the image self-similarity property and the final pixel estimation is the weighted average of all pixels which form similar patterns in the patches of the processing block. As this method yields excellent denoising results, an extension based on Block-Matching and 3D filtering (BM3D) exploiting the image local sparse representation in the transform domain was proposed 7 , 48 – 50 . These filters provide very satisfying results when the Gaussian noise is encountered, however, they are very sensitive to impulsive noise as the utilized similarity measures between groups of pixels (patches) are susceptible to the impact of outlying pixels and the filters tend to preserve impulsive distortions.…”

Section: Related Workmentioning

confidence: 99%

On the reduction of mixed Gaussian and impulsive noise in heavily corrupted color images

Smolka,

Kusnik,

Radlak

2023

Sci Rep

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In this paper, a novel approach to the mixed Gaussian and impulsive noise reduction in color images is proposed. The described denoising framework is based on the Non-Local Means (NLM) technique, which proved to efficiently suppress only the Gaussian noise. To circumvent the incapacity of the NLM filter to cope with impulsive distortions, a robust similarity measure between image patches, which is insensitive to the impact of impulsive corruption, was elaborated. To increase the effectiveness of the proposed approach, the blockwise NLM implementation was applied. However, instead of generating a stack of output images that are finally averaged, an aggregation strategy combining all weights assigned to pixels from the processing block was developed and proved to be more efficient. Based on the results of comparisons with the existing denoising schemes, it can be concluded that the novel filter yields satisfactory results when suppressing high-intensity mixed noise in color images. Using the proposed filter the image edges are well preserved and the details are retained, while impulsive noise is efficiently removed. Additionally, the computational burden is not significantly increased, compared with the classic NLM, which makes the proposed modification applicative for practical image denoising tasks.

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Section: Related Workmentioning

confidence: 99%

On the reduction of mixed Gaussian and impulsive noise in heavily corrupted color images

Smolka,

Kusnik,

Radlak

2023

Sci Rep

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show abstract

“…However, the texture appears overly smoothed in the final result. Jia et al [33] enhanced the BM3D algorithm by focusing on three areas: adaptive estimation of noise variance, domain transformation filtering, and non-linear filtering. While the model yielded improved visual outcomes, it resulted in excessively smooth edges.…”

Section: Traditional Denoising Algorithmsmentioning

confidence: 99%

Innovative Dual-Stage Blind Noise Reduction in Real-World Images Using Multi-Scale Convolutions and Dual Attention Mechanisms

Rahman,

Aamir,

Bhutto

et al. 2023

Symmetry

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The distribution of real noise in images can disrupt the inherent symmetry present in many natural visuals, thus making its effective removal a paramount challenge. However, traditional denoising methods often require tedious manual parameter tuning, and a significant portion of deep learning-driven techniques have proven inadequate for real noise. Moreover, the efficiency of end-to-end algorithms in restoring symmetrical patterns in noisy images remains questionable. To harness the principles of symmetry for improved denoising, we introduce a dual deep learning model with a focus on preserving and leveraging symmetrical patterns in real images. Our methodology operates in two stages. In the first, we estimate the noise level using a four-layer neural network, thereby aiming to capture the underlying symmetrical structures of the original image. To enhance the extraction of symmetrical features and overall network performance, a dual attention mechanism is employed before the final convolutional layer. This innovative module adaptively assigns weights to features across different channels, thus emphasizing symmetry-preserving elements. The subsequent phase is devoted to non-blind denoising. It integrates the estimated noise level and the original image, thus targeting the challenge of denoising while preserving symmetrical patterns. Here, a multi-scale architecture is used, thereby amalgamating image features into two branches. The first branch taps into dilation convolution, thus amplifying the receptive field without introducing new parameters and making it particularly adept at capturing broad symmetrical structures. In contrast, the second branch employs a standard convolutional layer to focus on finer symmetrical details. By harnessing varied receptive fields, our method can recognize and restore image symmetries across different scales. Crucial skip connections are embedded within this multi-scale setup, thus ensuring that symmetrical image data is retained as the network deepens. Experimental evaluations, conducted on four benchmark training sets and 12 test datasets, juxtaposed with over 20 contemporary models based on the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) metrics, underscore our model’s prowess in not only denoising but also in preserving and accentuating symmetrical elements, thereby setting a new gold standard in the field.

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“…The powerful Non-local Means (NLM) technique 37,38 utilizes the image self-similarity property and the final pixel estimation is the weighted average of all pixels which form similar patterns in the patches of the processing block. As this method yields excellent denoising results, an extension based on Block-matching and 3D filtering (BM3D) exploiting the image local sparse representation in the transform domain was proposed 7,[39][40][41] . These filters provide very satisfying results when the Gaussian noise is encountered, however, they are very sensitive to impulsive noise as the utilized similarity measures between groups of pixels (patches) are susceptible to the impact of outlying pixels and the filters tend to preserve impulsive distortions.…”

Section: Gaussian Noise Reductionmentioning

confidence: 99%

On the Reduction of Mixed Gaussian and Impulsive Noise in Heavily Corrupted Color Images

Smolka,

Kusnik,

Radlak

2023

Preprint

View full text Add to dashboard Cite

In this paper, a novel approach to the mixed Gaussian and impulsive noise reduction in color images is proposed. The described denoising framework is based on the Non-Local Means (NLM) technique, which proved to efficiently suppress only the Gaussian noise. To circumvent the incapacity of the NLM filter to cope with impulsive distortions, a robust similarity measure between image patches, which is insensitive to the impact of impulsive corruption, was elaborated. To increase the effectiveness of the proposed approach, the blockwise NLM implementation was applied. However, instead of generating a stack of output images that are finally averaged, an aggregation strategy combining all weights assigned to pixels from the processing block was developed and proved to be more efficient. Based on the results of comparisons with the existing denoising schemes, it can be concluded that the novel filter yields satisfactory results when suppressing high-intensity mixed noise in color images. Using the proposed filter the image edges are well preserved and the details are retained while impulsive noise is efficiently removed. Additionally, the computational burden is not significantly increased, compared with the classic NLM, which makes the proposed modification applicative for practical image denoising tasks.

show abstract

Blind-noise image denoising with block-matching domain transformation filtering and improved guided filtering

Cited by 7 publications

References 37 publications

On the reduction of mixed Gaussian and impulsive noise in heavily corrupted color images

On the reduction of mixed Gaussian and impulsive noise in heavily corrupted color images

Innovative Dual-Stage Blind Noise Reduction in Real-World Images Using Multi-Scale Convolutions and Dual Attention Mechanisms

On the Reduction of Mixed Gaussian and Impulsive Noise in Heavily Corrupted Color Images

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