Convolutional neural network-based detector for random-valued impulse noise

Xu, Shaoping; Zhang, Guizhen; Hu, Lingyan; Liu, Tingyun

doi:10.1117/1.jei.27.5.050501

Cited by 4 publications

(2 citation statements)

References 17 publications

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“…The above experiment results is in line with our previous theoretical analysis in the last paragraph of the second Chapter II-A. In order to correctly estimate the LS detection threshold of pixels in the flat area and the detailed area under different noise levels, we performed polynomial fitting on a large amount of data obtained from experiments to obtain the calculation formula of the LS threshold with respect to the image noise level, as shown in formulas (15) and (16):…”

Section: Selection Of Ls Thresholdsupporting

confidence: 78%

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A Two-Stage Algorithm for the Detection and Removal of Random-Valued Impulse Noise Based on Local Similarity

Lin

Feng

et al. 2020

IEEE Access

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A two-stage denoising algorithm based on local similarity is proposed to process lowly and moderate corrupted images with random-valued impulse noise in this paper. In the noise detection stage, the pixel to be detected is centered and the local similarity between the pixel and each pixel in its neighborhood is calculated, which can be used as the probability that the pixel is noise. By obtaining the local similarity of each pixel in the image and setting an appropriate threshold, the noise pixels and clean pixels in the damaged image can be detected. In the image restoration stage, an improved bilateral filter based on local similarity and geometric distance is designed. The pixel detected as noise in the first stage is filtered and the new intensity value is the weighted average of all pixel intensities in its neighborhood. A large number of experiments have been conducted on different test images and the results show that compared with the mainstream denoising algorithms, the proposed method can detect and filter out the random-value impulse noise in the image more effectively and faster, while better retaining the edges and other details of the image.

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Section: Selection Of Ls Thresholdsupporting

confidence: 78%

“…Step 3: Estimate the overall noise level of the original noisy image through (12)- (14) and then obtain the best LS detection thresholds for pixels in the flat area and the complex area through formulas (15) and (16).…”

Section: E Image Preprocessingmentioning

confidence: 99%