원문정보
초록
영어
This paper introduces a novel solution to generate a super-resolution image from a set of low-resolution input based on patch information. Recent research has shown that super-resolved data can be reconstructed from an extremely small set of measurements compared to that currently required. This paper incorporates the compressive sensing framework to the reconstruction model. Moreover, in order to remove outliers introduced by image parallax, the supervised patch-adaptive matching method which uses photometrical similarity and geometrical distance to determine the matching patch is proposed to reconstruct the high resolution image. The performance of the proposed algorithm on both synthetic and real images is evaluated with several grayscale and color image sequences and found successful when compared to other algorithms.
목차
1. Introduction
2. Compressive Sensing Model
2.1. Sparse Reconstruction Condition
2.2. Greedy Reconstruction Algorithm
3. Proposed Approach
3.1. Patch Adaptive Super Resolution
3.2. High Resolution Image Reconstruction
4. Experimental Results
5. Conclusion
References