Analysis of Two Representative Algorithms of Depth Estimation from Light Field Images

Abstract

Lightfield (LF) cameras offer many more of advanced features than conventional cameras. One type of LF camera, the lenslet LF camera is portable and has become available to consumers in recent years. Images from LF cameras can be used to generate depth maps which is an essential tool in several areas of image processing and can be used in the generation of various visual effects. LF images generated by lenslet LF cameras have different properties that images generated from an array of conventional cameras and thus require different depth estimation approaches. To study and compare the differences of depth estimation from LF images, this project describes two existing algorithms for depth estimation. The first algorithm, from Korea Advanced Institute of Science and Technology, estimates the depth labels based on stereo matching theory, where each label is corresponding to a specific depth. The second algorithm, developed by University of California and Adobe Systems Company, takes full advantage of the LF camera structure to estimate depths from so-called refocus cue and correspondence cue, and combines the depth maps from both cues in a Markov Random Field (MRF) to obtain a quality depth map. Since these two methods apply different concepts and contain some widely used techniques for depth estimations, it is worthy to analyze and compare their advantages and disadvantages. In this report, the two methods were implemented using public domain software, the first method being called the DEL method and the second being called the DER method. Comparisons with respect to computational speed and visual quality of the depth information show that the DEL method tend to be more stable and gives better results than the DER method for the experiments carried out in this report.