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.

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Neural Radiance Fields (NeRF) is a recent technology which had a large impact in computer vision, promising to generate high quality novel views and corresponding disparity map, all using a fairly small number of input images. In effect, they are a new way to represent a light field. In this paper, we compare NeRF with traditional light field methods for novel view synthesis and depth estimation, in an attempt to quantify the advantages brought by NeRF, and to put these results in perspective with the way both paradigms are used practically. We provide qualitative and quantitative comparisons, discuss them and highlight some aspects of working with NeRF depending on the type of light field data used.

In this work we explore methods for allowing advanced colour editing on light field images to be performed. This investigation is twofold. First we look at soft colour algorithms to decompose images into colour layers and the various ways it could be applied to light field data in order to ensure spatially consistent results. Then, with the purpose of colour editing in mind, we present an object-based layer separation method so that editing a layer does not wrongly affect specific objects. We further discuss the advantages and drawbacks that light field data present over regular single-view images for this purpose. Finally we present some editing results to show that our methods allow us to obtain visually appealing images that remain consistent across all light field views and minimise the colour artefacts inherent to layer decomposition methods.