Research On Super-resolution Image Reconstruction Algorithms Based On Multi-scale Similar Learning

The image super-resolution reconstruction refers to the process that reconstruct high-definition image from one or several low resolution images. The technology of super-resolution reconstruction was initially studied and applied in aerospace, meteorology,medicine and other neighborhood, but as the digital times is coming, it is moving towards themass, and gradually heading for greater application areas, therefore, it has great applicationprospects and society and economy value. Simultaneously, the topic of the technology ofsuper-resolution reconstruction still faces much more challenges at present, for it is an ill-posed problem and its involving technologies are wide, which is the major field in theresearch of image processing technology.This paper discusses the relative theories and methods of the image super-resolutionreconstruction, systematically and thoroughly studies all aspects of the image super-resolutionreconstruction, focuses on the image reconstruction in super-resolution based on the multi-scale similarity learning, and designs the relative high-performance algorithms. The researchdirections of this paper include:1. the application of the multi-scale similarity learning in thesingle-image super-resolution reconstruction;2. the application of the multi-scale similaritylearning in the multi-image super-resolution reconstruction;3. the design of super-resolutionreconstruction algorithm for high-performance images and video. In view of the aboveproblems, this paper sums up the main research contents and contributions as follows:1. It compares the super-resolution reconstruction algorithms based on the interpolation,regularization, multi-image reconstruction and learning, and on this basis, it focuses on thesuper-resolution reconstruction algorithm based on learning, including the neighborembedding algorithm based on the manifold learning and the super-resolution reconstructionalgorithm based on the sparse representation. In addition, it illustrates the multi-scale self-similarity learning can not only provide information directly to the image reconstruction, butit also can act as the idea of regularization in image reconstruction, and thus it puts forwardthe single image super-resolution reconstruction algorithm based on a combination of legendlearning and multi-scale self-similarity learning. The algorithm provides a framework, whichmake the priori gained from the legend learning and the information obtained from the selflearning work together by establishing a joint dictionary. Meanwhile, it introduces an iterativealgorithm, since considering that the similarity relation among blocks of graph istransmissible in the multi-scale space of image. The reconstructed image boundary is sharp, which can effectively suppress noise.2. It studies the relationship between the super-resolution magnification and thereconstructed image quality, and on this basis, it puts forward the multi-image super-resolution reconstruction algorithm based on sparse representation. The single-image super-resolution reconstruction is transferred into multi-image reconstruction by building the super-resolution reconstruction in the multi-scale image space, and compared to the singlereconstructed image; the reconstructed image quality by using an exclusive dictionary foreach level of the image reconstruction shows a significant improvement at the samemagnification conditions.3. It studies the similar information in the image sequence, illustrates that an existingcomplementary information shall be adopted if the same object or similar objects appear inmultiple images with the same or different scales, which provides the possibility of realizationof image super-resolution reconstruction, and on this basis, it puts forward the super-resolution reconstruction algorithm based on the multi-image similarity learning. Against thetraditional multi-image super-resolution reconstruction method, the algorithm improves theprecision of image registration by using the image splitting and multiple registration strategyto solve the problems of high-demanding image registration and unstable reconstructedquality; simultaneously, the neighbor embedding method based on learning is introduced inthe multi-image super-resolution reconstruction, so as to reduce the pixel filling link’sdemand for the image registration. The rise and fall make the algorithm not only own goodrobustness, but also improve the practical value.4. It explores the application of the super-resolution reconstruction technology based onthe learning in the video. Aimed at the high-demanding real-time video application, the super-resolution reconstruction algorithm of video based on GPU acceleration and sparserepresentation is putted forward. It focuses on the parallel optimization of the algorithm, theoptimization of data stream and resource utilization. In addition, compared to the existingCPU serial algorithm, the execution speed of the algorithm improves significantly by2ordersof magnitude by setting the video frame queue, improving the memory access concurrency,and designing and adopting the efficient parallel Principal Component Analysis fordimensionality reduction and similarity computing and other methods.