Research On Image Color Enhancement Method Based On Sparse Representation

The expression of an image is determined by the color and its brightness,and the color plays a vital role in people's observation.In the field of image processing,the color can usually simplify the extraction of scenes and objects as a powerful describer.Moreover,human beings can recognize tens of thousands of colors and brightness but can only recognize dozens of grayscale tones,therefore,the colorization has become an important research field in image enhancement.At present,the colorization is divided into two directions:colorization based on human intervention and colorization based on reference images.Among them,the colorization based on human intervention takes a lot of manpower,and its effect depends entirely on the person's choice for color,so there is a big limitation in practical application.However,the colorization based on reference images has become a trend of researches because of the natural colorized effect obtained without too much human intervention.In this paper,the current colorization is studied and analyzed.Inspired by the theory of machine learning algorithms,a conceptual colorization framework based on feature classification and detail enhancement is proposed.Based on this framework,two colorization algorithms are proposed:(1)Multi-sparse dictionary colorization algorithm based on the feature classification and detail enhancement(CEMDC).The algorithm is divided into three parts:Multi-sparse dictionary classification colorization algorithm(CMDC),Classification optimization based on local constraint(LCC)and Detail enhancement based on Laplacian Pyramid(LPDE).Experiments prove that the algorithm can achieve a natural colorized effect for a gray-scale image,and it is consistent with the human vision.Furthermore,the detail level of the colorization results is clearer,the number of false colorized pixels is obviously reduced.(2)Colorization algorithm based on local structure preserving and detail enhancement(LSPEC).The algorithm improves the colorization effect from the perspective of sparse representation and it makes full use of the similarities and dissimilarities of the structure to achieve more accurate sparse representation so as to obtain more accurate colorization effects and greatly reduce the number of false colorized pixels.At the same time,a detail enhancement algorithm based on the original gray-scale image(OGDE)is proposed.Compared with LPDE,this algorithm can completely preserve the structure and detail information of the original gray-scale image and solve the problems of missing details and blurred edges caused by the sparse representation.In summary,from two aspects of program design and algorithm design,the two algorithms in this paper improve the accuracy of feature classification,enhance the detail information of the image,and achieve a natural colorized effect consistent with the human vision,furthermore,they are suitable for visible grayscale images,grayscale fusion images,infrared images and other multi-domain colorization.