Multiple Parameters Determination for Image and Video Reconstruction Using Genetic Algorithm and Generalized Stein’s Unbiased Risk Estimation

This paper proposes a heuristic multi-parameter optimization method for image and video reconstruction. The method is embedded in an evolutionary computation framework in which a set of parameters are encoded into chromosomes of an individual, and a generalized Stein’s unbiased risk estimation (GSURE) is employed as a fitness function. By using the proposed method, the near-optimal parameters can be determined after several iterations, and with the use of these parameters, the ideal image can then be reconstructed. Furthermore, this method can be extended to video reconstruction. According to the similarity between adjacent frames of a video, the generation of population is vividly replaced by the frame index. We calculate the fitness based on the current frame, breed a new generation selectively, and use the new population to optimize continually based on the next frame. After several frames, the parameters can be optimized, and an ideal reconstructed frame can then be steadily output. Numerous experiments demonstrate the effectiveness and advantages of the proposed method.