On the visual quality enhancement of super-resolution images

Abstract

Super-resolution (SR) is the process of obtaining a higher resolution image from a set of lower resolution (LR) blurred and noisy images. One may, then, envision a scenario where a set of LR images is acquired with a sensor on a moving platform. In such a case, an SR image can be reconstructed in an area of sufficient overlap between the LR images which generally have a relative shift with respect to each other by subpixel amounts. The visual quality of the SR image is affected by many factors such as the optics blur, the inherent signalto- noise ratio of the system, quantization artifacts, the number of scenels (scene elements) i.e., the number of overlapped images used for SR reconstruction within the SR grid and their relative arrangement. In most cases of microscanning, the subpixel shifts between the LR images are pre-determined: hence the number of the scenels within the SR grid and their relative positions with respect to each other are known and, as a result, can be used in obtaining the reconstructed SR image with high quality. However, the LR images may have relative shifts that are unknown. This random pattern of subpixel shifts can lead to unpleasant visual quality, especially at the edges of the reconstructed SR image. Also, depending on the available number of the LR images and their relative positions, it may be possible to produce SR only along a single dimension diagonal, horizontal or vertical and use interpolation in the orthogonal dimension because there isn't sufficient information to produce a full 2D image. We investigate the impact of the number of overlapped regions and their relative arrangement on the quality of the SR images, and propose a technique that optimally allocates the available LR scenels to the SR grid in order to minimize the expected unpleasant visual artifacts.