Denoising Single Images by Feature Ensemble Revisited

Abstract

Image denoising is still a challenging issue in many computer vision subdomains. Recent studies have shown that significant improvements are possible in a supervised setting. However, a few challenges, such as spatial fidelity and cartoon-like smoothing, remain unresolved or decisively overlooked. Our study proposes a simple yet efficient architecture for the denoising problem that addresses the aforementioned issues. The proposed architecture revisits the concept of modular concatenation instead of long and deeper cascaded connections, to recover a cleaner approximation of the given image. We find that different modules can capture versatile representations, and a concatenated representation creates a richer subspace for low-level image restoration. The proposed architecture’s number of parameters remains smaller than in most of the previous networks and still achieves significant improvements over the current state-of-the-art networks.

RDASNet: Image Denoising via a Residual Dense Attention Similarity Network

“…However, due to the interference of imaging equipment and external factors, images are very easily affected by various noises and become blurred [ 1 ], so image denoising is particularly important. In fact, image denoising has always been a fundamental and important problem in computer vision [ 2 ]. Its purpose is to recover a clean image from a noisy image [ 3 ].…”