Multi-objective optimizing for image recovering in compressive sensing

Recently, compressive sensing theory has opened up a new path for the development of signal processing. According to this theory, a novel single pixel camera system has been introduced to overcome the current limitation and challenges of manufacturing large scale photo sensor arrays. In the system, image can be recovered from original image using random measurements by means of compressive sensing techniques. In the image recovering process, some default parameters are used. It is important to find optimizing to enhance image accuracy and recovering speed. Some images recovering algorithms were attempted to recover images and some parameters of the algorithms need be set appropriately during the recovering process. In order to find the better values of the parameters, in this paper, a multi-objective optimizing method is proposed. Accuracy and rapidity are selected for optimization goal, and a multi-objective fitness function is built. Then some optimization parameters are selected and the ranges of the parameters are decided. And genetic algorithm is used in the optimization process. Finally, the result of optimization is used in a single pixel camera system. And the results of recovering images are better than the default parameter´s for a single pixel camera experiment.