Recent Progress in Image Deblurring

This paper comprehensively reviews the recent development of image deblurring, including non-blind/blind, spatially invariant/variant deblurring techniques. Indeed, these techniques share the same objective of inferring a latent sharp image from one or several corresponding blurry images, while the blind deblurring techniques are also required to derive an accurate blur kernel. Considering the critical role of image restoration in modern imaging systems to provide high-quality images under complex environments such as motion, undesirable lighting conditions, and imperfect system components, image deblurring has attracted growing attention in recent years. From the viewpoint of how to handle the ill-posedness which is a crucial issue in deblurring tasks, existing methods can be grouped into five categories: Bayesian inference framework, variational methods, sparse representation-based methods, homography-based modeling, and region-based methods. In spite of achieving a certain level of development, image deblurring, especially the blind case, is limited in its success by complex application conditions which make the blur kernel hard to obtain and be spatially variant. We provide a holistic understanding and deep insight into image deblurring in this review. An analysis of the empirical evidence for representative methods, practical issues, as well as a discussion of promising future directions are also presented.Comment: 53 pages, 17 figure

Aperture Supervision for Monocular Depth Estimation

We present a novel method to train machine learning algorithms to estimate scene depths from a single image, by using the information provided by a camera's aperture as supervision. Prior works use a depth sensor's outputs or images of the same scene from alternate viewpoints as supervision, while our method instead uses images from the same viewpoint taken with a varying camera aperture. To enable learning algorithms to use aperture effects as supervision, we introduce two differentiable aperture rendering functions that use the input image and predicted depths to simulate the depth-of-field effects caused by real camera apertures. We train a monocular depth estimation network end-to-end to predict the scene depths that best explain these finite aperture images as defocus-blurred renderings of the input all-in-focus image.Comment: To appear at CVPR 2018 (updated to camera ready version

EventNeRF: Neural Radiance Fields from a Single Colour Event Camera

Asynchronously operating event cameras find many applications due to their high dynamic range, no motion blur, low latency and low data bandwidth. The field has seen remarkable progress during the last few years, and existing event-based 3D reconstruction approaches recover sparse point clouds of the scene. However, such sparsity is a limiting factor in many cases, especially in computer vision and graphics, that has not been addressed satisfactorily so far. Accordingly, this paper proposes the first approach for 3D-consistent, dense and photorealistic novel view synthesis using just a single colour event stream as input. At the core of our method is a neural radiance field trained entirely in a self-supervised manner from events while preserving the original resolution of the colour event channels. Next, our ray sampling strategy is tailored to events and allows for data-efficient training. At test, our method produces results in the RGB space at unprecedented quality. We evaluate our method qualitatively and quantitatively on several challenging synthetic and real scenes and show that it produces significantly denser and more visually appealing renderings than the existing methods. We also demonstrate robustness in challenging scenarios with fast motion and under low lighting conditions. We will release our dataset and our source code to facilitate the research field, see https://4dqv.mpi-inf.mpg.de/EventNeRF/.Comment: 18 pages, 18 figures, 3 table

EventNeRF: Neural Radiance Fields from a Single Colour Event Camera

Asynchronously operating event cameras find many applications due to theirhigh dynamic range, no motion blur, low latency and low data bandwidth. Thefield has seen remarkable progress during the last few years, and existingevent-based 3D reconstruction approaches recover sparse point clouds of thescene. However, such sparsity is a limiting factor in many cases, especially incomputer vision and graphics, that has not been addressed satisfactorily sofar. Accordingly, this paper proposes the first approach for 3D-consistent,dense and photorealistic novel view synthesis using just a single colour eventstream as input. At the core of our method is a neural radiance field trainedentirely in a self-supervised manner from events while preserving the originalresolution of the colour event channels. Next, our ray sampling strategy istailored to events and allows for data-efficient training. At test, our methodproduces results in the RGB space at unprecedented quality. We evaluate ourmethod qualitatively and quantitatively on several challenging synthetic andreal scenes and show that it produces significantly denser and more visuallyappealing renderings than the existing methods. We also demonstrate robustnessin challenging scenarios with fast motion and under low lighting conditions. Wewill release our dataset and our source code to facilitate the research field,see https://4dqv.mpi-inf.mpg.de/EventNeRF/.<br