Super-resolution for differently exposed mixed-resolution multi-view images adapted by a histogram matching method

Abstract

Super-resolution is an important task in the image and video processing domain. In mixed-resolution multi-view scenarios, neighboring high-resolution reference perspectives can be used to increase the image quality of a given low-resolution target view. By using corresponding depth information, the required high-frequency part can be projected from a reference view onto the image plane of the target perspective. However, the contrast and thus the amount of high-frequency information in a reference view varies with the cameras exposure settings. As a consequence, the resulting super-resolution quality drops in case of exposure time variations between the different views. By incorporating a histogram matching method, the required high-frequency part can be efficiently adapted to the exposure settings of the target view. The simulation results show that the proposed adaption leads to an average PSNR gain of 0.63 dB for differently exposed mixed-resolution multi-view setups.