Super-resolution Reconstruction Based On Adaboost Algorithm

Super-resolution image reconstruction is the technology to reconstruct high resolution image by fusing redundancy information in multiple similar but different low resolution images. Because super-resolution reconstruction uses the image processing technology to obtain high resolution image without upgrading system hardware, it has been vastly used in image-processing areas.In some areas, it has a high requirement for the speed of super-resolution reconstruction. The main work of this thesis is to learn and achieve super-resolution reconstruction based on adaboost algorithm, which is a fast super-resolution reconstruction algorithm.Specific work of this thesis is concentrated in the following four parties. Firstly, the concept and connotation of super-resolution reconstruction are elaborated, and the POCS method and MAP method are analyzed. Secondly, based on the analysis and comparison of a variety of search algorithm in block-matching motion estimation, this thesis proposes an improved SEA motion estimation algorithm, which has a faster speed that compares with SEA algorithm, but ensures the accuracy and reliability of motion estimation. Thirdly, research and analysis of the Adaboost learning algorithm, and propose the concrete realization of super-resolution reconstruction based on adaboost algorithm. Finally, by a software system compiled on the VC++6.0, perform the Adaboost-based super-resolution reconstruction algorithm and the POCS algorithm that as a comparison.