Research On Multiframe Image Super-resolution Reconstruction Technique Based On Prior Constraint

Super-resolution image reconstruction from multiple inter-displacement and information-complemented low-resolution images is one of the research focuses in the image processing area, since a better-resolution image can be obtained based on this technique without changing current low-resolution imaging systems. It has comprehensive applications in the domain of penal reconnaissance, target recognition, medical diagnosis and so on.Starting with analyzing the degradation mechanism of images, this thesis focuses on image denoise, image registration and super-resolution reconstruction models and algorithms with the constraint of prior information. The main work is as follows.Firstly, this paper analyzed the degradation mechanism of imaging systems. The fuzzy identification issue of the imaging system is discussed. An effective method of estimating the system degradation point spread function is introduced based on real measurement data.Secondly, The characteristics of the salt and pepper noise was analyzed. The solution sparseness acted as the prior information. A new extended model was proposed with corresponding algorithm. Experiments showed that the model has a significant denoising effect on images with salt and pepper noise while maintaining important features of the images.Thirdly, the paper proposed a fast image registration algorithm based on wavelet transform. A method was given to select the sub-map effectively by using the efficiency sub-maps in place of the whole map to carry out registration. The t-test method was employed to filter the sun-maps. The formula to measure the similarity was simplified, and the wavelet coefficients features was also analyzed. Based on the multi-resolution analysis features of the wavelet transform, a fast registration algorithm using layered and iterated strategy was proposed. This method is proved to be much better than traditional methods in the aspects of both the accuracy and the calculation capacity.Fourthly, this paper proposed a method of super-resolution reconstruction using parasitic ripple as a priori information. The mechanism of parasitic ripple generation was studied. Based on the parasitic ripple caused by registration error, the PSF estimation error and noise, a super-resolution model was adopted by adding a penalty item of parasitic ripple which can restrain the parasitic ripple. In accordance with the effect of the parasitic ripple on different regions, a method of calculating the adaptive regularization parameter with which to suppress parasitic ripple was given. Compared with traditional smoothing-bound method, the new method can suppress the parasitic ripple without affecting the reconstruction of the edge of the image. Finally, the paper proposed a method of super-resolution reconstruction to enhance the image quality with the grey level distribution information of the prior image. The defects of the current super-resolution reconstruction methods were analyzed. Two methods of constructing a priori image were introduced. A priori image was applied to restraint the results of the super-resolution images. A new super-resolution model was constructed using the principle of minimum identification information to obtain a reconstructed image. The strategy based on confidence level gave an adaptive scheme to select the regularization parameters. When the grey level distribution information of the prior image is more silimar to that of the real image, the reconstruction results can be improved to a greater extent; at the same time, compared with traditional methods , the grey level distribution characteristics restraint proposed by the method mentioned above is proved to be simpler and more effective.