Content-Adaptive Light Field Contrast Enhancement Using Focal Stack and Hierarchical Network

Abstract

Light field (LF) cameras can capture a scene’s information from all different directions and provide comprehensive image information. However, the resulting data processing commonly encounters problems of low contrast and low image quality. In this article, we put forward a content-adaptive light field contrast enhancement scheme using a focal stack (FS) and hierarchical structure. The proposed FS set contained 300 light field images, which were captured using a Lytro-Illum camera. In addition, we integrated the classical Stanford Lytro Light Field Archive and JPEG Pleno Database. Specifically, according to the global brightness, the acquired LF images were classified into four different categories. First, we transformed the original LF FS into a depth map (DMAP) and all-in-focus (AIF) image. The image category was preliminarily determined depending on the brightness information. Then, the adaptive parameters were acquired by the corresponding multilayer perceptron (MLP) network training, which intrinsically enhanced the contrast and adjusted the light field image. Finally, our method automatically produced an enhanced FS based on the DMAP and AIF image. The experimental comparison results demonstrate that the adaptive values predicted by our MLP had high precision and approached the ground truth. Moreover, compared to existing contrast enhancement methods, our method provides a global contrast enhancement, which improves, without over-enhancing, local areas. The complexity of image processing is reduced, and real-time, adaptive LF enhancement is realized.