Image Super-Resolution Based on Sparsity Prior via Smoothed $l_0$ Norm
This work addresses image enhancement for applications like medical imaging or surveillance, but it is incremental as it builds on existing sparse representation methods.
The paper tackles single image super-resolution by proposing an algorithm using smoothed l0-norm to find jointly sparse representations, achieving improved reconstruction quality with higher PSNR and SSIM scores for most images.
In this paper we aim to tackle the problem of reconstructing a high-resolution image from a single low-resolution input image, known as single image super-resolution. In the literature, sparse representation has been used to address this problem, where it is assumed that both low-resolution and high-resolution images share the same sparse representation over a pair of coupled jointly trained dictionaries. This assumption enables us to use the compressed sensing theory to find the jointly sparse representation via the low-resolution image and then use it to recover the high-resolution image. However, sparse representation of a signal over a known dictionary is an ill-posed, combinatorial optimization problem. Here we propose an algorithm that adopts the smoothed $l_0$-norm (SL0) approach to find the jointly sparse representation. Improved quality of the reconstructed image is obtained for most images in terms of both peak signal-to-noise-ratio (PSNR) and structural similarity (SSIM) measures.