Abstract:

Based on super-resolution image reconstruction algorithm of sparse representation,this paper presents an improved use of Locality-constrained Linear Coding (LLC)super-resolution reconstruction algorithm that can improve the quality of the image reconstruction while reducing the computational complexity and improving processing speed,which achieves good results.First a pair of high and low resolution Dictionary was trained in accordance with a set of high-resolution images,then LLC method was used to encode the input image with the low resolution dictionary.A high-resolution image was preliminarily reconstructed with high-resolution dictionary and coding coefficient,and finally a global constraint was added to rebuild the high-resolution image.The experimenttal results show that the new algorithm, comparing to the criginal algorithm, not only improves the quality of image reconstruction, but also reduces the computational complexity.The above improved algorithm has also been extended and applied to multi-frame image super-resolution reconstruction. Analysis and experiments show that this super-resolution reconstruction algorithm achieves some good results, it can improve the quality of the image reconstruction while reduce the computational complexity with improved processing speed.

Key words: super-resolution, LLC, dictionary training, sparse representation