Improving dictionary based image super-resolution with nonlocal total variation regularization

The dictionary based super-resolution (SR) approach has received much attention in recent years because sparse representation is very effective for image restoration tasks. By sparse representation, original image patches are represented as a sparse linear combination of atoms in an over-complete dictionary. However, the dictionary based SR approach has some disadvantages that it produces some ringing artifacts especially along the object boundaries and is not effective in reconstructing images which contain the patterns with strong edge. In this paper, we improve the dictionary based SR using nonlocal total variation regularization. In the training stage, we jointly train two dictionaries, D_h and D_l, from the low-resolution (LR) and high-resolution (HR) training data sets by using KSVD algorithm as in conventional methods. In the synthesis stage, we obtain the sparse coefficient vector from the LR test image over the LR dictionary, and reconstruct SR image patches using the coefficient vectors. Then, we employ nonlocal total variation regularization to remove annoying ringing artifacts and recover the patterns with strong edge in images. Experimental results on various test images demonstrate that the proposed method is very effective in enhancing the dictionary based SR approaches in terms of quantitative performance and visual quality.