Details Preserving Deep Collaborative Filtering-Based Method for Image Denoising
This addresses the issue of detail loss in image denoising for applications requiring high visual fidelity, though it appears incremental as it builds on existing neural network approaches.
The paper tackles the problem of image denoising algorithms failing to preserve fine details due to a smooth-out effect, proposing a deep collaborative filtering-based method that achieves better quantitative (PSNR, SSIM) and qualitative visual performance than many state-of-the-art algorithms.
In spite of the improvements achieved by the several denoising algorithms over the years, many of them still fail at preserving the fine details of the image after denoising. This is as a result of the smooth-out effect they have on the images. Most neural network-based algorithms have achieved better quantitative performance than the classical denoising algorithms. However, they also suffer from qualitative (visual) performance as a result of the smooth-out effect. In this paper, we propose an algorithm to address this shortcoming. We propose a deep collaborative filtering-based (Deep-CoFiB) algorithm for image denoising. This algorithm performs collaborative denoising of image patches in the sparse domain using a set of optimized neural network models. This results in a fast algorithm that is able to excellently obtain a trade-off between noise removal and details preservation. Extensive experiments show that the DeepCoFiB performed quantitatively (in terms of PSNR and SSIM) and qualitatively (visually) better than many of the state-of-the-art denoising algorithms.