Research Of Adaptive Compressed Sensing Image Restoration Algprithm

Compressed Sensing Theory has opened up new ideas of information sampling as a newsampling theory, it has vast potential for future development and has high value on research.Compressed sensing is mainly based on the sparsity and compressibility of the signal, and theimplementation process can be divided into three stages: Process of sparse signals, Signalmeasurement, signal reconstruction.The signal reconstruction process of Compressed Sensing Theory is actually a process ofsolving underdetermined equations, the sparsity of the signal makes this equations solvable,There are a lot of practical reconstruction algorithm and each of them has its own serviceable rangeand Limitation. So the reconstruction algorithm of Compressed Sensing Theory is the hot spotand difficulty of the theory.The research work of this paper is to propose improvement measures based on matchingpursuit like algorithm to make the algorithm more efficient to implement. Two adaptivestrategies as follows will be proposed based on ROMP algorithm.The first one is the number of cycles observed adaptive program based on ROMP algorithm.This algorithm is base on the relationship between subject cycles and noise ratio of signal andpropose the self-adaption exit condition of the iteration. The experiment has show us theself-adaption reconstruction in this way, the second improvement program a self-adaptionsuggestion base on the result analysis of each iteration. This improvement analogy cauchy’s testfor convergence,In this algorithm,we also introduce a dynamic iteration step mechanism for thefurther efficiency of algorithm execution to achieve a good reconstructionwe also shows the practical application of image restoration using the optimized algorithmwe have proposed. The advantages and disadvantages of the algorithm will be analyzed,we willpoint out the scope of application of the algorithm and some suggestions for furtherimprovement at the end of this paper.