A novel image zooming method based on sparse representation of Weber's law descriptor

A novel image zooming algorithm based on sparse representation of Weber's law descriptor is proposed in this article. It is known that features of low resolution can be extracted using four one-dimensional filters convoluting with low resolution patches. Weber's law descriptor can well deal with local feature, so we extract low-resolution image feature replacing one-dimensional with Weber's law descriptor in the four filters. In addition, fractional calculus can deal with nonlocal information such as texture. For avoiding small complex component when the size of image is not an odd integer, we modify the extending image method used by Bai, so it can save lots of calculation. The proposed approach combining the Weber's law descriptor with fractional calculus achieves a very good performance. Experimental results show that our method can well eliminate jagged effect when up-sampling an image and is robustness to noise.