Light field imaging has emerged as a technology allowing to capture richer visual information from our world. As opposed to traditional photography, which captures a 2D projection of the light in the scene integrating the angular domain, light fields collect radiance from rays in all directions, demultiplexing the angular information lost in conventional photography. On the one hand, this higher dimensional representation of visual data offers powerful capabilities for scene understanding, and substantially improves the performance of traditional computer vision problems such as depth sensing, post-capture refocusing, segmentation, video stabilization, material classification, etc. On the other hand, the high-dimensionality of light fields also brings up new challenges in terms of data capture, data compression, content editing, and display. Taking these two elements together, research in light field image processing has become increasingly popular in the computer vision, computer graphics, and signal processing communities. In this paper, we present a comprehensive overview and discussion of research in this field over the past 20 years. We focus on all aspects of light field image processing, including basic light field representation and theory, acquisition, super-resolution, depth estimation, compression, editing, processing algorithms for light field display, and computer vision applications of light field data.



@Article{Wu2017light, Title = {Light Field Image Processing: An Overview}, Author = {Gaochang Wu and Belen Masia and Adrian Jarabo and Yuchen Zhang and Liangyong Wang and Qionghai Dai and Tianyou Chai and Yebin Liu}, Journal = {IEEE Journal of Selected Topics in Signal Processing}, Year = {2017}, Volume = {11}, Number = {7}, }




This work was supported by the National Key Foundation for exploring scientific instrument No.2013YQ140517, the National NSF of China grant No.61522111 and No.61531014. It was also partially supported by the Spanish Ministry of Economy and Competitiveness (TIN2016-79710-P), and the European Research Council (Consolidator Grant, project CHAMELEON, ref. 682080). This work is also supported by Science and Technology Planning Project of Guangdong Province, China (2015B010105004).