Light Field Super-Resolution Using Cross-Resolution Input Based on PatchMatch and Learning Method.

This paper addresses the problem of generating a super-resolution (SR) image from a low-resolution (LR) image assisted by nearby high-resolution (HR) image. The scaling factor of the super-resolved image is up to 8 times and even more, which is much larger than the ordinary super-resolution scaling factor. Combined patch match and learning based method for image super-resolution using a cross-resolution input. The method is used to super-resolve the images captured by a hybrid light field system consisting of a standard LF camera and a HR DSLR camera. We take the central high-resolution image as a reference, to deal with the around low-resolution images. Unlike other relative algorithm, the proposed method is exploited for the existence of large parallax between the captured images. The main process of our method is a combination of patch based (i.e., example based) algorithm and learning based (e.g., convolutional neural network) method, and does not require any calibration information. Experimental results show that our proposed method performs better than existing method on challenging scenes containing complex texture, specularity and large parallax. Both accuracy and visual improvements in our results are noticeable.