Learning-Based Single Image Super-Resolution with Improved Edge Information

Abstract

A new learning-based single image super-resolution technique that upscales the low resolution (LR) image in a single pass toits desired high resolution (HR) image is proposed here.Inthe upscaling procedure, a linearmapping function is learned from the external data set. Mapping function converts LR patch to its corresponding patch.In most of the patch-based learning technique, smoothness of the overlapped regions is performed with an average value of the overlapped regions. As a result, edge information that reflects in adjacent LR patches does not always transparently reflects in HR patches. So in our technique, we applied edge directed smoothness in adjacent patches. An edge exists along the direction, where the second-order derivative is lower. To reach this, we have selected non-overlapping patch, and after getting HR patch, we performed edge directed smoothness of adjacent patches. This results smoothness of adjacent patches with more detailedge information. Apart from this nonoverlapping patch selection reduces computational complexity, without compromising image quality. Experimental results show significant improvement in terms of subjective and objective quality than other popular learning or interpolation based method.Our method showsrobustness on noisy images also.