Blind Denoising Using Dense in Dense Network with Attention Module
Jing-Ming Guo, Della Fitrayani Budiono, Yi‐Chong Zeng, Zhen-Yu Chen · 2025
Effective denoising is fundamental in image restoration, significantly impacting downstream computer vision tasks. Conventional CNN-based denoising models rely on paired training data of clean and noisy images, yet clean images are often unavailable in practical settings. Recent advancements in blind denoising, such as Noise2Void (N2V) and blind-spot networks, enable training without clean images; however, denoising quality remains an area for improvement. This paper introduces a novel Dense-in-Dense Network with Attention (DiDNA) designed specifically for blind denoising. By leveraging dense connections within a dense architecture and an attention module, DiDNA effectively captures complex noise patterns and enhances denoising capability. Experimental evaluations demonstrate that DiDNA not only surpasses existing blind denoising methods but also achieves competitive performance with traditional paired denoisers across CNN-based and non-CNN-based approaches.