# A Convex Similarity Index for Sparse Recovery of Missing Image Samples

## TL;DR

This paper introduces a new convex perceptual similarity index called CSIM for sparse recovery of missing image samples, enabling globally optimal solutions with improved image reconstruction quality.

## Contribution

It proposes a convex similarity measure based on SSIM, along with an ADMM-based algorithm for sparse recovery that guarantees convergence to the global optimum.

## Key findings

- CSIM outperforms traditional metrics like MSE in image recovery tasks.
- The proposed method guarantees convergence to the global optimum.
- Simulation results validate the effectiveness of CSIM and the recovery algorithm.

## Abstract

This paper investigates the problem of recovering missing samples using methods based on sparse representation adapted especially for image signals. Instead of $l_2$-norm or Mean Square Error (MSE), a new perceptual quality measure is used as the similarity criterion between the original and the reconstructed images. The proposed criterion called Convex SIMilarity (CSIM) index is a modified version of the Structural SIMilarity (SSIM) index, which despite its predecessor, is convex and uni-modal. We derive mathematical properties for the proposed index and show how to optimally choose the parameters of the proposed criterion, investigating the Restricted Isometry (RIP) and error-sensitivity properties. We also propose an iterative sparse recovery method based on a constrained $l_1$-norm minimization problem, incorporating CSIM as the fidelity criterion. The resulting convex optimization problem is solved via an algorithm based on Alternating Direction Method of Multipliers (ADMM). Taking advantage of the convexity of the CSIM index, we also prove the convergence of the algorithm to the globally optimal solution of the proposed optimization problem, starting from any arbitrary point. Simulation results confirm the performance of the new similarity index as well as the proposed algorithm for missing sample recovery of image patch signals.