Study on the method of colour image noise reduction based on optimal channel‐processing

Wang, Yutan; Dai, Yingpeng; Liu, Xiangnan; Liu, Bohan; Guo, Xiaoqing

doi:10.1049/iet-ipr.2017.0871

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Cited by 3 publications

(2 citation statements)

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“…Other ED methods can also be proposed with applying different image smoothing methods during the IHT as in [36]. Also, it is highly recommended to include the communication channel type and noise on the transmitted images [37].…”

Section: Resultsmentioning

confidence: 99%

Optimised halftoning and inverse halftoning of dermoscopic images for supporting teledermoscopy system

Ashour

Guo

Hawas

et al. 2019

IET Image Processing

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Section: Resultsmentioning

confidence: 99%

Optimised halftoning and inverse halftoning of dermoscopic images for supporting teledermoscopy system

Ashour

Guo

Hawas

et al. 2019

IET Image Processing

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“…Images are easily affected by various factors during acquisition and transmission, making the acquired images often contain noise [1][2]. The noise introduced in the image acquisition process is mainly due to the working condition of the image sensor influenced by the environment or the quality of the sensor itself [3]. Image denoising is a hot issue in the field of image processing research.…”

Section: Introductionmentioning

confidence: 99%

Optimization of image processing methods based on wavelet transform and adaptive thresholding

Chen

2023

Applied Mathematics and Nonlinear Sciences

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This paper first classifies the image noise and evaluates the image quality by means of the correlation function, mean square error value, and fidelity. Secondly, an image adaptive threshold denoising system based on wavelet transform is constructed, and the image processing is realized by using the wavelet transform principle and the selection of threshold value. Finally, the image is optimized by using the modulo-maximum denoising method and the threshold denoising method for empirical analysis. The results show that the signal-to-noise ratio of the noisy signal is 6.2315dB, and the signal-to-noise ratio of the modulo-maximum processing is 12.7024 dB. The peak signal-to-noise ratio of the noisy image is 20.1258dB, the peak signal-to-noise ratio of the soft threshold denoising method is 26.4831dB, and the peak signal-to-noise ratio of the hard threshold denoising method is 22.5864dB. This shows that the wavelet transform and adaptive thresholding can effectively denoise and ensure image quality. Image quality.

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Performance comparison of deep convolution neural network and median filter in terms of denoising and detail preserving

Azawi

2023

Multimed Tools Appl

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Study on the method of colour image noise reduction based on optimal channel‐processing

Cited by 3 publications

References 8 publications

Optimised halftoning and inverse halftoning of dermoscopic images for supporting teledermoscopy system

Optimised halftoning and inverse halftoning of dermoscopic images for supporting teledermoscopy system

Optimization of image processing methods based on wavelet transform and adaptive thresholding

Performance comparison of deep convolution neural network and median filter in terms of denoising and detail preserving

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