Gaussian Noise Removal of Blind Source Separation Based on Image Sequence

Dong, Shijia; Yu, Chunyu; Sun, Bin

doi:10.1109/iscid.2018.10102

Cited by 4 publications

(1 citation statement)

References 14 publications

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“…In cases where noise severely interferes with the image, it affects signal recognition and visual quality. Techniques like blind source separation have been used to recover images heavily disturbed by noise, although their performance may be influenced by the number of image samples [6].…”

Section: Related Researchmentioning

confidence: 99%

Advances in image denoising techniques: a comprehensive review

2024

Second International Conference on Electrical, Electronics, and Information Engineering (EEIE 2023)

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In the field of computer vision, improving image quality has always been a significant research direction. Image Denoising techniques reduce the impact of various noises on images, thereby enhancing image quality. This paper provides a comprehensive review of existing image denoising methods and research. Through relevant studies, the influence of image noise on image quality is discussed. Traditional denoising methods are compared with deep learning methods, highlighting the advantages of deep learning denoising methods in handling complex noise. Furthermore, various denoising methods are categorized and their denoising principles are discussed in detail. Subsequently, a detailed overview of deep learning in the field of image denoising is presented. A comparison of various denoising methods is conducted, and their pros and cons are summarized. The paper also offers prospects for future research development. In conclusion, this paper summarizes various image denoising methods and provides insights into future developments in the field of image denoising.

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Section: Related Researchmentioning

confidence: 99%