  Over the years, progressive improvements in denoising performance have been
achieved by several image denoising algorithms that have been proposed. Despite
this, many of these state-of-the-art algorithms tend to smooth out the denoised
image resulting in the loss of some image details after denoising. Many also
distort images of lower resolution resulting in a partial or complete
structural loss. In this paper, we address these shortcomings by proposing a
collaborative filtering-based (CoFiB) denoising algorithm. Our proposed
algorithm performs weighted sparse domain collaborative denoising by taking
advantage of the fact that similar patches tend to have similar sparse
representations in the sparse domain. This gives our algorithm the intelligence
to strike a balance between image detail preservation and noise removal. Our
extensive experiments showed that our proposed CoFiB algorithm does not only
preserve the image details but also perform excellently for images of any given
resolution where many denoising algorithms tend to struggle, specifically at
low resolutions.
