关键词：图像片；光场；光场相机；特征分析
摘 要：In this thesis, a new scheme, \textit{scene geometry-aware image-patch modeling}, based on the concept of a \textbf{patch-cube}, is proposed to model image patches in a light field, rather than in a single image. A light field is a collection of images all acquired at the same instant, providing a set of perspectives on the scene as though observing all of the light information that passes through a windowing portal (clearly with some discretization and sampling). The scene geometric information is implicitly incorporated in our modeling process, including depth and occlusion, without explicit knowledge of 3D scene structure. These extra constraints on the scene geometry empower our learned features to be less affected by image noise, lighting conditions, etc. As demonstration, we apply our method to joint image denoising and joint spatial/angular image super-resolution tasks, where its use of the light field will be seen to permit it to outperform its image-patch based counterparts. Here, a 2D camera array with small incremental baselines is used to capture the light field data, and this analysis is the majority of what we report. Additionally, working with real data from real light-field cameras, we present novel and highly effective methods for the calibration of these camera arrays.