Research For Image Super Resolution Reconstruction And Interpolation Algorithm

Image super-resolution reconstruction (SRR) refers to a resolution enhancement technology, which produce the image of high-resolution (HR) image from a set of degraded low-resolution (LR) images. This dissertation investigates image SRR algorithm and the key issues of interpolation algorithm.With the demand of HR, the images produced from low cost acquisition system need the signal and image processing technology to improve the quality, so SRR has become one of the hot areas of the research on image. Among this, the high-precision movement registration algorithm, the blind reconstruction algorithms, and stable of fast and the effective algorithm are the focus of the difficulties of SRR.In this paper, Firstly, the mathematical model of SRR is introduced, and several classical SRR algorithms are discussed. Frequency algorithms and spatial algorithms are the two main reconstruction technique. Now, Bayesian approach, projections onto convex sets (POCS) Approach, MAP-POCS hybrid approach and iterative back projection (IBP) approach are wide used in SRR algorithms. Secondly, LR image must be inserted into the HR image grid, so image interpolation theory is investigated. Polynomial interpolation, as nearest neighbor interpolation, bi1inear interpolation, cubic interpolation, has the inherent drawbacks, so we adopt spline Interpolation, kernel regression interpolation. Finally, simulation results of the HR image show the quality of there algorithm.We adopt the regularization term to solve the ill-posed inverse problem of SRR. With the current wide spread use of norm 2 as the regularization, classical kernel regression (CKR) method was utilized for image reconstruction. We propose the novel weight function related to the position of the random pixels in the scale of the sample window. Then steering kernel regression (SKR) method contained local orientation was used to improve the quality of the reconstruction image. Simulation results demonstrated the improvement of image quality and the reduction of root mean square error (RMSE).