The Richardson-Lucy (RL) algorithm is a well-known nonlinear restoration method and has been widely applied in the fields of astronomical image restoration, microscopic image restoration, and so on because of its capability of generating high-quality restoration results and potential in realizing super-resolution. However, when being applied to restore the wavefront coded blurry images, the classical RL algorithm converges very slowly and has to be iterated many times before obtaining a satisfactory result, which severely prohibits its real-time application. Vector-extrapolation-based RL algorithm was invented to solve this problem, but the noise amplification increases fast, and additional post-processing is needed to further improve the signal-to-noise ratio. Therefore, in this paper, Hui Zhao’s research team propose an improved RL algorithm by introducing an exponential modified correction term into the framework of the original vector-extrapolation-based RL algorithm. It not only results in a bigger iteration step, which ensures a faster convergence can be obtained, but also the noise amplification is effectively prohibited. Besides that, they design a structure-similarity-index-metric-based stopping criterion, based on which the optimum number of iterations for each color channel is obtained. Experimental results reveal that the total iterations decreases approximately 78.9%, and the restored images demonstrate a superior visual quality without denoising additionally.
（Original research article "Applied Optics Vol. 58, Issue 13, pp. 3630-3638 (2019) https://www.osapublishing.org/ao/abstract.cfm?uri=ao-58-13-3630"）
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