2018/08/16 by Pierre Matysiak, Mairéad Grogan, Matysiak, Pierre +8
Computer Science · Mathematics · #Advanced Image Processing Techniques #Advanced Vision and Imaging #Artificial intelligence #Computer Vision and Pattern Recognition (cs.CV) #Computer graphics (images) #Computer science #Computer vision #FOS: Computer and information sciences #Field (mathematics) #Ghosting #Image (mathematics) #Image Enhancement Techniques #Light field #Mathematics #Noise (video) #Pipeline (software) #Process (computing) #cs.CV
paper · pdf · doi:10.48550/arxiv.1808.05387
published in arXiv (Cornell University) (Cornell University) · IEEE International Conference on Image Processing 2018, 5 pages, 7 figures
arxiv created 2018/08/16 · openalex publication_date 2018/08/16 · arxiv updated 2018/08/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
In recent years, light fields have become a major research topic and their applications span across the entire spectrum of classical image processing. Among the different methods used to capture a light field are the lenslet cameras, such as those developed by Lytro. While these cameras give a lot of freedom to the user, they also create light field views that suffer from a number of artefacts. As a result, it is common to ignore a significant subset of these views when doing high-level light field processing. We propose a pipeline to process light field views, first with an enhanced processing of RAW images to extract subaperture images, then a colour correction process using a recent colour transfer algorithm, and finally a denoising process using a state of the art light field denoising approach. We show that our method improves the light field quality on many levels, by reducing ghosting artefacts and noise, as well as retrieving more accurate and homogeneous colours across the sub-aperture images.