  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.
