Light field image processing : overview and research issues

Light field (LF) imaging first appeared in the computer graphics community with the goal of photorealistic 3D rendering [1]. Motivated by a variety of potential applications in various domains (e.g., computational photography, augmented reality, light field microscopy, medical imaging, 3D robotic, particle image velocimetry), imaging from real light fields has recently gained in popularity, both at the research and industrial level.peer-reviewe

Application for light field inpainting

Light Field (LF) imaging is a multimedia technology that can provide more immersive experience when visualizing a multimedia content with higher levels of realism compared to conventional imaging technologies. This technology is mainly promising for Virtual Reality (VR) since it displays real-world scenes in a way that users can experience the captured scenes in every position and every angle, due to its 4-dimensional LF representation. For these reasons, LF is a fast-growing technology, with so many topics to explore, being the LF inpainting the one that was explored in this dissertation. Image inpainting is an editing technique that allows synthesizing alternative content to fill in holes in an image. It is commonly used to fill missing parts in a scene and restore damaged images such that the modifications are correct and visually realistic. Applying traditional 2D inpainting techniques straightforwardly to LFs is very unlikely to result in a consistent inpainting in its all 4 dimensions. Usually, to inpaint a 4D LF content, 2D inpainting algorithms are used to inpaint a particular point of view and then 4D inpainting propagation algorithms propagate the inpainted result for the whole 4D LF data. Based on this idea of 4D inpainting propagation, some 4D LF inpainting techniques have been recently proposed in the literature. Therefore, this dissertation proposes to design and implement an LF inpainting application that can be used by the public that desire to work in this field and/or explore and edit LFs.Campos de luz é uma tecnologia multimédia que fornece uma experiência mais imersiva ao visualizar conteúdo multimédia com níveis mais altos de realismo, comparando a tecnologias convencionais de imagem. Esta tecnologia é promissora, principalmente para Realidade Virtual, pois exibe cenas capturadas do mundo real de forma que utilizadores as possam experimentar em todas as posições e ângulos, devido à sua representação em 4 dimensões. Por isso, esta é tecnologia em rápido crescimento, com tantos tópicos para explorar, sendo o inpainting o explorado nesta dissertação. Inpainting de imagens é uma técnica de edição, permitindo sintetizar conteúdo alternativo para preencher lacunas numa imagem. Comumente usado para preencher partes que faltam numa cena e restaurar imagens danificadas, de forma que as modificações sejam corretas e visualmente realistas. É muito improvável que aplicar técnicas tradicionais de inpainting 2D diretamente a campos de luz resulte num inpainting consistente em todas as suas 4 dimensões. Normalmente, para fazer inpainting num conteúdo 4D de campos de luz, os algoritmos de inpainting 2D são usados para fazer inpainting de um ponto de vista específico e, seguidamente, os algoritmos de propagação de inpainting 4D propagam o resultado do inpainting para todos os dados do campo de luz 4D. Com base nessa ideia de propagação de inpainting 4D, algumas técnicas foram recentemente propostas na literatura. Assim, esta dissertação propõe-se a conceber e implementar uma aplicação de inpainting de campos de luz que possa ser utilizada pelo público que pretenda trabalhar nesta área e/ou explorar e editar campos de luz

Light field image processing: an overview

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

Learning to Predict Image-based Rendering Artifacts with Respect to a Hidden Reference Image

Image metrics predict the perceived per-pixel difference between a reference image and its degraded (e. g., re-rendered) version. In several important applications, the reference image is not available and image metrics cannot be applied. We devise a neural network architecture and training procedure that allows predicting the MSE, SSIM or VGG16 image difference from the distorted image alone while the reference is not observed. This is enabled by two insights: The first is to inject sufficiently many un-distorted natural image patches, which can be found in arbitrary amounts and are known to have no perceivable difference to themselves. This avoids false positives. The second is to balance the learning, where it is carefully made sure that all image errors are equally likely, avoiding false negatives. Surprisingly, we observe, that the resulting no-reference metric, subjectively, can even perform better than the reference-based one, as it had to become robust against mis-alignments. We evaluate the effectiveness of our approach in an image-based rendering context, both quantitatively and qualitatively. Finally, we demonstrate two applications which reduce light field capture time and provide guidance for interactive depth adjustment.Comment: 13 pages, 11 figure

PlenoPatch: patch-based plenoptic image manipulation

Patch-based image synthesis methods have been successfully applied for various editing tasks on still images, videos and stereo pairs. In this work we extend patch-based synthesis to plenoptic images captured by consumer-level lenselet-based devices for interactive, efficient light field editing. In our method the light field is represented as a set of images captured from different viewpoints. We decompose the central view into different depth layers, and present it to the user for specifying the editing goals. Given an editing task, our method performs patch-based image synthesis on all affected layers of the central view, and then propagates the edits to all other views. Interaction is done through a conventional 2D image editing user interface that is familiar to novice users. Our method correctly handles object boundary occlusion with semi-transparency, thus can generate more realistic results than previous methods. We demonstrate compelling results on a wide range of applications such as hole-filling, object reshuffling and resizing, changing object depth, light field upscaling and parallax magnification

Fast Learning Radiance Fields by Shooting Much Fewer Rays

Learning radiance fields has shown remarkable results for novel view synthesis. The learning procedure usually costs lots of time, which motivates the latest methods to speed up the learning procedure by learning without neural networks or using more efficient data structures. However, these specially designed approaches do not work for most of radiance fields based methods. To resolve this issue, we introduce a general strategy to speed up the learning procedure for almost all radiance fields based methods. Our key idea is to reduce the redundancy by shooting much fewer rays in the multi-view volume rendering procedure which is the base for almost all radiance fields based methods. We find that shooting rays at pixels with dramatic color change not only significantly reduces the training burden but also barely affects the accuracy of the learned radiance fields. In addition, we also adaptively subdivide each view into a quadtree according to the average rendering error in each node in the tree, which makes us dynamically shoot more rays in more complex regions with larger rendering error. We evaluate our method with different radiance fields based methods under the widely used benchmarks. Experimental results show that our method achieves comparable accuracy to the state-of-the-art with much faster training.Comment: Accepted by lEEE Transactions on lmage Processing 2023. Project Page: https://zparquet.github.io/Fast-Learning . Code: https://github.com/zParquet/Fast-Learnin

Light field reconstruction from multi-view images

Kang Han studied recovering the 3D world from multi-view images. He proposed several algorithms to deal with occlusions in depth estimation and effective representations in view rendering. the proposed algorithms can be used for many innovative applications based on machine intelligence, such as autonomous driving and Metaverse