Low illumination fog noise image denoising method based on ACE-GPM

• Published in: PloS one Vol. 19; no. 5; p. e0302492
• Main Authors: Li, Wuyi, Zhou, Guanglu, Wang, Xingjian
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 07.05.2024; Public Library of Science (PLoS)
• Subjects: Algorithms; Analysis; Color; Computer and Information Sciences; Engineering and Technology; Entropy (Information theory); Expenditures; Fog; Health aspects; Humans; Image contrast; Image enhancement; Image Enhancement - methods; Image Processing, Computer-Assisted - methods; Image quality; Information processing; Information technology; Light; Lighting - methods; Machine vision; Methods; Neural networks; Noise; Noise control; Noise reduction; Partial differential equations; Physical Sciences; Research and Analysis Methods; Research methodology; Signal-To-Noise Ratio; Wavelet transforms; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0302492

The Perona-Malik (P-M) model exhibits deficiencies such as noise amplification, new noise introduction, and significant gradient effects when processing noisy images. To address these issues, this paper proposes an image-denoising algorithm, ACE-GPM, which integrates an Automatic Color Equalization (ACE) algorithm with a gradient-adjusted P-M model. Initially, the ACE algorithm is employed to enhance the contrast of low-light images obscured by fog and noise. Subsequently, the Otsu method, a technique to find the optimal threshold based on between-class variance, is applied for precise segmentation, enabling more accurate identification of different regions within the image. After that, distinct gradients enhance the image’s foreground and background via an enhancement function that accentuates edge and detailed information. The denoising process is finalized by applying the gradient P-M model, employing a gradient descent approach to further emphasize image edges and details. Experimental evidence indicates that the proposed ACE-GPM algorithm not only elevates image contrast and eliminates noise more effectively than other denoising methods but also preserves image details and texture information, evidenced by an average increase of 0.42 in the information entropy value. Moreover, the proposed solution achieves these outcomes with reduced computational resource expenditures while maintaining high image quality.