Fast Convergence Strategy for Multi-Image Superresolution via Adaptive Line Search

Abstract

Multi-image superresolution (SR) techniques produce a high-resolution image from several low-resolution observations. Previous reconstruction-based SR approaches focus more on the optimization models but have not adequately emphasized the mathematic-solving techniques for this typically illconditioned and under-determined large scale problem. Since step size plays an important role in the iterative SR process, and there is a tradeoff between less computation cost and higher accuracy, conventional SR methods either adopt a fixed step size to obtain a higher running speed, or use a computationally expensive line search algorithm to pursue an improvement in accuracy. Taking both cues into consideration, in this paper, we propose an adaptive line search strategy to realize the fast convergence of reconstruction-based SR. The approximate analytical expression of step size is introduced to prevent us from setting it empirically or running iterations to test a proper one. We further modify the proposed strategy to be more adaptive under different SR conditions. Using our strategy, one can accelerate the SR process and obtain the optimal solution with less iteration. Experiments are conducted on both synthetic data sets and realworld scenes. The results have demonstrated the effectiveness and outperformance of our proposed strategy compared with other line search strategies.

Keywords

• Adaptive line search
• approximate analytical expression
• fast convergence
• step size
• superresolution