16 accepted papers
Image sensors used in real camera systems are equipped with colour filter arrays which sample the light rays in different spectral bands. Each colour channel can thus be obtained sep-arately by considering the corresponding colour filter. While existing compression solutions mostly assume that the c…
Neural implicit representations have appeared as a promising technique for representing a variety of signals, among which light fields. These representations offer several advantages over traditional grid-based representations, such as independence to the signal resolution. Some work has been done t…
In this paper, we propose a novel light field compression method based on a low rank-constrained neural scene representation. While most existing methods directly compress the light field views, our method first learns a Multi-Layer Perceptron (MLP)-based Neural Radiance Field (NeRF) from the input…
This paper presents a novel unrolled optimization method to reconstruct a dense light field from a focal stack containing only very few images captured with different focus. The proposed unrolled method first reconstructs Fourier Disparity Layers (FDL) from which all the light field viewpoints can t…
We present a simple variational approach for reconstructing color light fields (LFs) in the compressed sensing (CS) framework with very low sampling ratio, using both coded masks and color filter arrays (CFAs). A coded mask is placed in front of the camera sensor to optically modulate incoming rays,…
The recently introduced plenoptic point cloud representation marries a 3D point cloud with a light field. Instead of each point being associated with a single colour value, there can be multiple values to represent the colour at that point as perceived from different viewpoints. This representation…
In this paper, we present a learning-based framework for light field view synthesis from a subset of input views. Building upon a light-weight optical flow estimation network to obtain depth maps, our method employs two reconstruction modules in pixel and feature domains respectively. For the pixel-…
The Deep Image Prior has been recently introduced to solve inverse problems in image processing with no need for training data other than the image itself. However, the original training algorithm of the Deep Image Prior constrains the reconstructed image to be on a manifold described by a convoluti…
This paper proposes a learning based solution to disparity (depth) estimation for either densely or sparsely sampled light fields. Disparity between stereo pairs among a sparse subset of anchor views is first estimated by a fine-tuned FlowNet 2.0 network adapted to disparity prediction task. These c…
Light fields are 4D signals capturing rich information from a scene. The availability of multiple views enables scene depth estimation, that can be used to generate 3D point clouds. The constructed 3D point clouds, however, generally contain distortions and artefacts primarily caused by inaccuracies…
In this paper, we present a novel light field denoising algorithm using a vector-valued regularization operating in the 4D ray space. More precisely, the method performs a PDE-based anisotropic diffusion along directions defined by local structures in the 4D ray space. It does not require prior esti…
This paper explores the problem of learning transforms for image compression via autoencoders. Usually, the rate-distortion performances of image compression are tuned by varying the quantization step size. In the case of autoencoders, this in principle would require learning one transform per rate-…
In this paper, we explore the use of graph-based transforms to capture correlation in light fields. We consider a scheme in which view synthesis is used as a first step to exploit inter-view correlation. Local graph-based transforms (GT) are then considered for energy compaction of the residue signa…
This paper studies the problem of low rank approximation of light fields for compression. A homography-based approximation method is proposed which jointly searches for homographies to align the different views of the light field together with the low rank approximation matrices. We first consider a…
This paper addresses the problem of image compression using sparse representations. We propose a variant of autoencoder called Stochastic Winner-Take-All Auto-Encoder (SWTA AE). “Winner-Take-All” means that image patches compete with one another when computing their sparse representation and “Stocha…
The High Efficiency Video Coding standard (HEVC) supports a total of 35 intra prediction modes which aim at reducing spatial redundancy by exploiting pixel correlation within a local neighborhood. In this paper, we show that spatial correlation remains after intra prediction, leading to high energy…