The Realizition And Optimization Research For Image Inpainting Algorithm Based On Sparse Representation

Image inpainting is an important technology in the area of digital imageprocessing. The core idea is to make full use of active information to estimate theunknown area of image so that the repaired image will be close to or achieving to thevisual effect of original image. This technology has very strong practicability in manyapplication areas. In recent years, sparse representation theory has become a noveltyresearch direction in image restoration technology. Therefore, this paper based on thestudy for theory of sparse representation and combined with the latest research resultsof image restoration algorithm deeply study from the following angles: algorithmimplementation, algorithm efficiency and performance optimization. Main researchwork of this paper is as follows:1.This thesis has introduced the basic concept of sparse representation theory,discussed the main algorithm to solve the problem of sparse approximation, andproposed a method so that computational complexity is greatly reduced whenadopting LI norm to approximately replace LO norm for simplifying sparserepresentation expression.2.It constructs image inpaiting model which is based on sparse representation.First of all, we establish a constraint mathematical model upon linear primitivecombination from the part and the whole. Thus the energy function of the imagerestoration will be established with nice sparseness as well as the energy function willbe optimized in minimization. Secondly, we respectively set up the primitives fromthe discrete cosine transform (DCT) and the adaptive learning method, then do ananalysis and comparison among the different methods. Finally, the specific flow of image restoration algorithm is elaborated in detail from building primitivecombination, order of iflling and restoration algorithms.3.This paper proposes an image inpaiting algorithm based on color informationand gradient difference. Adding color information, algorithm changes the order ofrepairing block. Meanwhile the size of repairing window is adjusted by thecomplexity of repairing block to improve Criminisi traditional algorithm. Sorestoration time is also greatly reduced.4.At the matter of boundary restoration, this text introduces a minimum errorboundary algorithm based on particle swarm optimization, which solves the problemsof residual and bad visual effect in the traditional image boundary restoration methodeffectively as well as improving the efficiency of the algorithm and repair effect.Finally, the results of Matlab simulation show that the image restorationalgorithm based on sparse representation purposed in this paper has obviouslyimprovement compared with the traditional image restoration method from threeaspects, which is as follows: the realization of algorithm, the efficiency of code andthe visual effect after optimization. It has some extent reference value for research onimage inpainting.