Image Denoising Via Collaborative Support-Agnostic Recovery
This addresses image quality improvement for applications like photography or medical imaging, but it appears incremental as it builds on existing sparse reconstruction techniques.
The paper tackled image denoising by proposing a collaborative support-agnostic sparse reconstruction algorithm that groups similar patches to estimate shared support taps, resulting in superior performance in SSIM and PSNR compared to state-of-the-art methods.
In this paper, we propose a novel image denoising algorithm using collaborative support-agnostic sparse reconstruction. An observed image is first divided into patches. Similarly structured patches are grouped together to be utilized for collaborative processing. In the proposed collaborative schemes, similar patches are assumed to share the same support taps. For sparse reconstruction, the likelihood of a tap being active in a patch is computed and refined through a collaboration process with other similar patches in the same group. This provides very good patch support estimation, hence enhancing the quality of image restoration. Performance comparisons with state-of-the-art algorithms, in terms of SSIM and PSNR, demonstrate the superiority of the proposed algorithm.