Advances in High Dynamic Range Imaging Using Deep Learning

Abstract

Natural scenes have a wide range of brightness, from dark starry nights to bright sunlit beaches. Our human eyes can perceive such a vast range of illumination through various adaptation techniques, thus allowing us to enjoy them. Contrarily, digital cameras can capture a limited brightness range due to their sensor limitations. Often, the dynamic range of the scene far exceeds the hardware limit of standard digital camera sensors. In such scenarios, the resulting photos will consist of saturated regions, either too dark or too bright to visually comprehend. An easy to deploy and widely used algorithmic solution to this problem is to merge multiple Low Dynamic Range (LDR) images captured with varying exposures into a single High Dynamic Range (HDR) image. Such a fusion process is simple for static sequences that have no camera or object motion. However, in most practical situations, a certain amount of camera and object motions are inevitable, leading to ghost-like artifacts in the final fused result. The process of fusing the LDR images without such ghosting artifacts is known as HDR deghosting. In this thesis, we make several contributions to the literature on HDR deghosting. First, we present a novel method to utilize auxiliary motion segmentation for efficient HDR deghosting... newline