Salt and pepper noise removal method based on stationary Framelet transform with non‐convex sparsity regularization

Chen, Yingpin; Huang, Yihan; Wang, Lingzhi; Huang, Huiying; Song, Jianhua; Yu, Chaoqun; Xu, Yingke

doi:10.1049/ipr2.12451

Cited by 10 publications

(5 citation statements)

References 61 publications

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“…Next, we verified the parameter sensitivity of the model by changing the values of the key parameters. To better observe the performance of the proposed model, we also compared the two‐stage filter (TSF) [52], the stationary framelet transform with

{l}_p

quasi‐norm (SFT_Lp) [53] and the iterative mean filter (IMF) [54], each measured based on the peak signal‐to‐noise ratio (PSNR) [55] and structural similarity index (SSIM) [56].…”

Section: Methodsmentioning

confidence: 99%

Salt‐and‐pepper denoising method for colour images based on tensor low‐rank prior and implicit regularization

Zhang

Wang

et al. 2022

IET Image Processing

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Most of the information obtained by humans comes from colour images. However, saltand-pepper noise (SPN) during signal acquisition, encoding, transmission, and decoding easily interferes with the quality of colour images. Most existing SPN denoising methods decompose a colour image into three independent matrices according to the colour channel and then recover each channel signal independently, ignoring the strong data correlation between channels. In addition, most existing SPN denoising methods apply only a single model-driven or data-driven approach and fail to take the advantages of their combination fully. Therefore, we first regard a colour image contaminated by SPN as the sum of an SPN tensor and a tensor with missing data. In this manner, we transform the denoising problem into a low-rank tensor reconstruction problem. We then introduce a model-driven-based parallel matrix factorization low-rank tensor reconstruction algorithm and a data-driven-based FFDNet denoising network to restore the colour image better. The proposed method not only enhances the similarity of the colour image channels but also explores the deep prior of the colour image to capture the image details. Finally, the proposed method is compared with some advanced denoising methods. The results show that the proposed method achieves a competitive denoising performance.

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{l}_p

quasi‐norm (SFT_Lp) [53] and the iterative mean filter (IMF) [54], each measured based on the peak signal‐to‐noise ratio (PSNR) [55] and structural similarity index (SSIM) [56].…”

Section: Methodsmentioning

confidence: 99%

Salt‐and‐pepper denoising method for colour images based on tensor low‐rank prior and implicit regularization

Zhang

Wang

et al. 2022

IET Image Processing

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“…As expected, image data are frequently subject to noise (such as analog electrical transmission), so a variety of algorithms can be applied to enhance the image quality, as shown in Figure 11. Therefore, noise removal is a crucial step in the processing of digital images [15], [16]. Certain digital image processing techniques are noise-sensitive.…”

Section: Introductionmentioning

confidence: 99%

Spatial domain noise removal filtering for low-resolution digital images

Salah,

Al-Sit,

Salah

et al. 2024

IJEECS

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<p>n this research work, six different filters are applied on a low resolution 8 b/pixel gray-scale images, which operate on small sub-images (windows of 3×3 to 11×11 pixels). The enhanced images are used to compare the efficiency of the different six filters using the peak signal to noise ratio (PSNR) image quality measure. Noise peak elimination filter (PSNR)=36.63) outperforms others, such as median filter (PSNR=36.61), while corruption estimation (PSNR=36.03) significantly cuts processing time by only processing the corrupted pixels while maintaining image details. Mean filter (PSNR=34.05) is sensitive to outliers, which cause the image's sharpness and fine features to be lost. By avoiding averaging across edges, bimodal-averaging filter (PSNR=35.30), which improves on the mean filter, chooses the mean of the biggest population. The median-mean filtering (PSNR=36.32), which combines median and mean filters and determines the output pixel by averaging the median and some nearby pixels, is another improvement above averaging.</p>

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“…Moreover, the MCA framework is used to separate the observed data into the cartoon part with low‐frequency components, texture part with high‐frequency components and sparse interference. Because the stationary Framelet transform [39] does not down‐sample the processed signal, it avoids the block artefacts in the Wavelet transform (WT) [40] caused by the Mallat algorithm [41] and achieves a better signal representation than WT. Therefore, we employ the stationary Framelet transform for cartoon and texture separation.…”

Section: Introductionmentioning

confidence: 99%

Robust sparse time‐frequency analysis for data missing scenarios

Chen

Huang

Song

2023

IET Signal Processing

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Sparse time-frequency analysis (STFA) can precisely achieve the spectrum of the local truncated signal. However, when the signal is disturbed by unexpected data loss, STFA cannot distinguish effective signals from missing data interferences. To address this issue and establish a robust STFA model for time-frequency analysis (TFA) in data loss scenarios, a stationary Framelet transform-based morphological component analysis is introduced in the STFA. In the proposed model, the processed signal is regarded as a sum of the cartoon, texture and data-missing parts. The cartoon and texture parts are reconstructed independently by taking advantage of the stationary Framelet transform. Then, the signal is reconstructed for STFA. The forward-backwards splitting method is employed to split the robust STFA model into the data recovery and robust timefrequency imaging stages. The two stages are then solved separately by using the alternating direction method of multipliers (ADMM). Finally, several experiments are conducted to show the performance of the proposed robust STFA method under different data loss levels, and it is compared with some existing state-of-the-art time-frequency methods. The results indicate that the proposed method outperforms the compared methods in obtaining the sparse spectrum of the effective signal when data are missing.The proposed method has a potential value in TFA in scenarios where data is easily lost.

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Salt and pepper noise removal method based on stationary Framelet transform with non‐convex sparsity regularization

Cited by 10 publications

References 61 publications

Salt‐and‐pepper denoising method for colour images based on tensor low‐rank prior and implicit regularization

Salt‐and‐pepper denoising method for colour images based on tensor low‐rank prior and implicit regularization

Spatial domain noise removal filtering for low-resolution digital images

Robust sparse time‐frequency analysis for data missing scenarios

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