Research On Sparse-Based Video Image Super-Resolution Reconstruction Algorithms

In the field of image super resolution, we often apply single low-resolution image or multiple lower-resolution images, combined with digital signal technology, to increasing image resolution. For video image, my research is to use correspondonding relationship between an object at different frame images to complete information reconstruction of pre-processed super-resolution image. In addition, combined with sparse representation, motion estimation and dictionary built related algorithms, my research improvs reconstruction algorithm of super-resolution video image based sparse representation.This paper combine MSA and Lucas Kanade, which makes up for their shortcomings to obtain the final block matching information. Moreover, results show that the method improves accuracy of match between image blocks steadily, and in comparison with tradition algorithm, the method increase peak signal to noise ratio and structral similarity.The paper improve feature extraction in the training phase of over dictionary and dimension reduction. In the feature extraction process, I combine second derivative and gradient direction to construct a new drop method. With the new drop method, I design an algorithm to improve gradient method. Importantly, in the aspects of convergence speed and feature extraction, the new algorithm is better than the gradient method. In addition, I improve two-dimensional principal component analysis(2DPCA) to reduce dimension, which can eliminate correspondece between rows and columns of images. Results suggest that the new method make image reconstruction effect more clearly and reconstruction speed faster. In the aspect of motion estimation.In order to evaluate the quality of the improved algorithm, my research explores the related algorithm of image quality assessment, and then I propose a new objective method, which uses the feature of highly sensitive human eyes, to extract edge feature from different directions and different amplitudes. A large amount of experiments demonstrate the effectiveness of the algorithm. Combined with results of super-resolution reconstruction, we can prove the effectiveness of super-resolution reconstruction algorithm in the study.