Research On Multi-frame Super-resolution Image Reconstruction

In recent years, Super-Resolution image reconstruction, which is also called the second-generation problem of image restoration, is an active topic in image processing field. As an embranchment of image fusion, this technique fuses the low resolution image sequence at pixel level to achieve higher resolution image. This technique could go beyond the resolution limit of imaging systems at a very low price. It is not only theoretically important in image processing and reconstruction field, but also practically urgent in many fields.This thesis mainly works on the algorithms of multi-frame super resolution reconstruction based on kernel regression. The main work is as following:1. The basic theory of the super-resolution image reconstruction is discussed, which consists of its history, state of arts, and its application potential in many fields; The imaging model and its description in mathematics are studied; Its feasibilities both in frequency domain and spatial domain are analyzed; Several classic spatial domain algorithms are also studied.2. The application of interpolation in image super resolution reconstruction is analyzed. Several classic interpolation algorithms are studied. Based on the analysis of these algorithms, concerning of the differences of region features in one image, a region-based bicubic image interpolation algorithm is proposed. Expeimental results show that this algorithm could reduce the runtime by about 11.86% while keeps better quality of the interpolated images.3. The kernel regression, as an effective tool in image processing and reconstruction field, is deeply studied. The regression and kernel regression theory are discussed. According to the shortcomings of the iterative steering kernel regression algorithm proposed by Takeda, a criteria to stop the iteration is introduced; concerning of the texture differences in one image, two revised algorithms are proposed based on median filtering and region adapting. Experimental results confirm the effectiveness of the revised algorithms.4. The applications in multi-frame super-resolution reconstruction of the kernel regression are studied. The principle of the multi-frame super-resolution reconstruction is analyzed; The motion estimation and commonly-used block-based motion estimation methods are studied. Then the two revised kernel regression algorithms based on median filtering and region adapting are applied to multi-frame super-resolution reconstruction. Experimental results confirm their effectiveness.Finally, we summarize our research work, and discuss further research topics and directions in the future.