Super-resolution and De-noising for Portrait Images Using Compressive Sensing
Zhu Qiuyu, Yichun Li · 2013
This paper proposes a novel solution to realize super-resolution and de-noising for portrait images. Considering that compressive sensing has a good performance on protecting and extracting information in images, it is involved to improve super-resolution. Image blocking is carried out in the process of establishing over-complete dictionaries. After vectorizing all blocks of training samples with different resolutions, the low-resolution and high-resolution over-complete dictionaries turn out by means of placing vectors by pair and correspondingly. On this basis, sparse coefficients of each low-resolution image can be worked out through measurement and OMP algorithm. Depending upon these coefficients, the desired high-resolution image can be constructed. Additionally a well-chosen sparsity is always an important factor that simplifies calculations and gets rid of noises. The experimental result illustrates the effectiveness and robustness of the novel solution.