An Advanced Noise Reduction and Edge Enhancement Algorithm

• Published in: Sensors Vol. 21; no. 16; p. 5391
• Main Authors: Huang, Shih-Chia, Hoang, Quoc-Viet, Le, Trung-Hieu, Peng, Yan-Tsung, Huang, Ching-Chun, Zhang, Cheng, Fung, Benjamin C. M., Cheng, Kai-Han, Huang, Sha-Wo
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 10.08.2021; MDPI
• Subjects: Algorithms; Chemical technology; contrast enhancement; Datasets; Decomposition; deep image prior; Deep learning; edge enhancement; Image Enhancement; Methods; Noise; noise removal; Signal-To-Noise Ratio; TP1-1185
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s21165391

Complementary metal-oxide-semiconductor (CMOS) image sensors can cause noise in images collected or transmitted in unfavorable environments, especially low-illumination scenarios. Numerous approaches have been developed to solve the problem of image noise removal. However, producing natural and high-quality denoised images remains a crucial challenge. To meet this challenge, we introduce a novel approach for image denoising with the following three main contributions. First, we devise a deep image prior-based module that can produce a noise-reduced image as well as a contrast-enhanced denoised one from a noisy input image. Second, the produced images are passed through a proposed image fusion (IF) module based on Laplacian pyramid decomposition to combine them and prevent noise amplification and color shift. Finally, we introduce a progressive refinement (PR) module, which adopts the summed-area tables to take advantage of spatially correlated information for edge and image quality enhancement. Qualitative and quantitative evaluations demonstrate the efficiency, superiority, and robustness of our proposed method.