Ni Karl S, Nguyen Truong Q
Video Processing Laboratory, Electrical and Computer Engineering Department, University of California, San Diego, CA 92093-0407 USA.
IEEE Trans Image Process. 2007 Jun;16(6):1596-610. doi: 10.1109/tip.2007.896644.
A thorough investigation of the application of support vector regression (SVR) to the superresolution problem is conducted through various frameworks. Prior to the study, the SVR problem is enhanced by finding the optimal kernel. This is done by formulating the kernel learning problem in SVR form as a convex optimization problem, specifically a semi-definite programming (SDP) problem. An additional constraint is added to reduce the SDP to a quadratically constrained quadratic programming (QCQP) problem. After this optimization, investigation of the relevancy of SVR to superresolution proceeds with the possibility of using a single and general support vector regression for all image content, and the results are impressive for small training sets. This idea is improved upon by observing structural properties in the discrete cosine transform (DCT) domain to aid in learning the regression. Further improvement involves a combination of classification and SVR-based techniques, extending works in resolution synthesis. This method, termed kernel resolution synthesis, uses specific regressors for isolated image content to describe the domain through a partitioned look of the vector space, thereby yielding good results.
通过各种框架对支持向量回归（SVR）在超分辨率问题中的应用进行了全面研究。在研究之前，通过找到最优核来增强SVR问题。这是通过将SVR形式的核学习问题表述为凸优化问题，具体来说是半定规划（SDP）问题来实现的。添加了一个额外的约束，将SDP简化为二次约束二次规划（QCQP）问题。经过这种优化后，研究了SVR与超分辨率的相关性，探讨了对所有图像内容使用单一通用支持向量回归的可能性，并且对于小训练集的结果令人印象深刻。通过观察离散余弦变换（DCT）域中的结构特性来辅助回归学习，对这一想法进行了改进。进一步的改进涉及分类和基于SVR的技术的结合，扩展了分辨率合成方面的工作。这种方法称为核分辨率合成，它使用针对孤立图像内容的特定回归器，通过对向量空间的分区观察来描述域，从而产生良好的结果。
