Research On Sparse Representation Based Single Color Image Super-resolution Reconstruction Algorithm

The purpose of image super-resolution reconstruction is to reconstruct a high-resolution image or sequence from a single image or multiple low-resolution images which is based on signal processing techniques.lt can be classified into single image super-resolution reconstruction and multiple images super-resolution reconstruction. This thesis focus on single image super-resolution reconstruction algorithms.Sparse representation based single super-resolution reconstruction technique is one of the hot topics in the field of image processing.The key technologies include sparse matrix computing and dictionary construction as well as related post-processing techniques. Based on the analysis of dictionary construction algorithms’performance and the effect of the training sample sets on reconstruction quality,an improved color image super-resolution reconstruction method based on sparse representation is proposed,and a post-processing algorithm is used to enhance reconstructed images.The main work are as follows:1) Performance verification of the dictionary construction algorithms.Two typical dictionary construction algorithms are implemented and the verification tests are conducted.The experimental results show that the KSVD algorithm outperforms MOD algorithm in respect of the relative number of correctly recovered atoms and the average representation error.2) Influence analysis of training sample sets and dictionary construction algorithms on the performance of super-resolution reconstruction.The construction of dictionary needs training sample sets, the training sets generally include two categories:(1) natural images training set;(2) the training set related to the image which is to be reconstructed. Two types of dictionaries are constructed respectively from two training sets with KSVD and MOD algorithms, and super-resolution reconstruction tests are conducted based on the two trained dictionaries. The experiment results show that reconstructed images based on the dictionary trained by the KSVD algorithm are better than the one trained by the MOD algorithm in respect of the peak signal to noise ratio(PSNR) and structural similarity (SSIM),and the more similar training set to the image which is to be reconstructed, the higher PSNR and SSIM are. 3) Proposing an improved method of color image super-resolution reconstruction algorithm based on sparse representation.The image to be reconstructed is firstly converted the RGB mode to the YCbCr mode,then the Y, Cb and Cr channel dictionaries are trained with the KSVD algorithm,and finally super-resolution reconstruction are performed on the three channels. The experiment results show that, compared with the separate Y component of the reconstruction algorithm and the R, G and B channel reconstruction algorithm, the proposed algorithm has better performance on PSNR and SSIM.4) Using a geometric locally adaptive sharpening (GLAS) based image post-processing algorithm to enhance the reconstructed image. For the reason that the reconstructed image edge is always blurred, a GLAS algorithm is used to enhance the image edges. The kernel functions are constructed according to the different shapes and sizes of the local structure of the image,and then are used to perform anisotropic filtering on the input image.The experimental results show that post-processing is necessary and can improve the reconstructed image quality numerically and visually.