MultiMedia Modeling

Although sparse coding error has been introduced to improve the performance of sparse representation-based image denoising, however, the sparse coding noise is not tight enough. To suppress the sparse coding noise, we exploit a couple of images to estimate unknown sparse code. There are two main contributions in this paper: The first is to use a reference denoised image and an intermediate denoised image to estimate the sparse coding coefficients of the original image. The second is that we set a threshold to rule out blocks of low similarity to improve the accuracy of estimation. Our experimental results have shown improvements over several state-of-the-art denoising methods on a collection of 12 generic natural images.