Light field super resolution through controlled micro-shifts of light field sensor

Light field cameras enable new capabilities, such as post-capture refocusing and aperture control, through capturing directional and spatial distribution of light rays in space. Micro-lens array based light field camera design is often preferred due to its light transmission efficiency, cost-effectiveness and compactness. One drawback of the micro-lens array based light field cameras is low spatial resolution due to the fact that a single sensor is shared to capture both spatial and angular information. To address the low spatial resolution issue, we present a light field imaging approach, where multiple light fields are captured and fused to improve the spatial resolution. For each capture, the light field sensor is shifted by a pre-determined fraction of a micro-lens size using an XY translation stage for optimal performance

Fast Sublinear Sparse Representation using Shallow Tree Matching Pursuit

Sparse approximations using highly over-complete dictionaries is a state-of-the-art tool for many imaging applications including denoising, super-resolution, compressive sensing, light-field analysis, and object recognition. Unfortunately, the applicability of such methods is severely hampered by the computational burden of sparse approximation: these algorithms are linear or super-linear in both the data dimensionality and size of the dictionary. We propose a framework for learning the hierarchical structure of over-complete dictionaries that enables fast computation of sparse representations. Our method builds on tree-based strategies for nearest neighbor matching, and presents domain-specific enhancements that are highly efficient for the analysis of image patches. Contrary to most popular methods for building spatial data structures, out methods rely on shallow, balanced trees with relatively few layers. We show an extensive array of experiments on several applications such as image denoising/superresolution, compressive video/light-field sensing where we practically achieve 100-1000x speedup (with a less than 1dB loss in accuracy)

Spatial and Angular Resolution Enhancement of Light Fields Using Convolutional Neural Networks

Light field imaging extends the traditional photography by capturing both spatial and angular distribution of light, which enables new capabilities, including post-capture refocusing, post-capture aperture control, and depth estimation from a single shot. Micro-lens array (MLA) based light field cameras offer a cost-effective approach to capture light field. A major drawback of MLA based light field cameras is low spatial resolution, which is due to the fact that a single image sensor is shared to capture both spatial and angular information. In this paper, we present a learning based light field enhancement approach. Both spatial and angular resolution of captured light field is enhanced using convolutional neural networks. The proposed method is tested with real light field data captured with a Lytro light field camera, clearly demonstrating spatial and angular resolution improvement

Graph-Based Light Field Super-Resolution

Light field cameras can capture the 3D information in a scene with a single exposure. This special feature makes light field cameras very appealing for a variety of applications: from post capture refocus, to depth estimation and image-based rendering. However, light field cameras exhibit a very limited spatial resolution, which should therefore be increased by computational methods. Off-the-shelf single-frame and multi-frame super-resolution algorithms are not ideal for light field data, as they ignore its particular structure. A few super-resolution algorithms explicitly devised for light field data exist, but they exhibit significant limitations, such as the need to carry out an explicit disparity estimation step for one or several light field views. In this work we present a new light field super-resolution algorithm meant to address these limitations. We adopt a multi- frame alike super-resolution approach, where the information in the different light field views is used to augment the spatial resolution of the whole light field. In particular, we show that coupling the multi-frame paradigma with a graph regularizer that enforces the light field structure permits to avoid the costly and challenging disparity estimation step. Our experiments show that the proposed method compares favorably to the state-of-the- art for light field super-resolution algorithms, both in terms of PSNR and visual quality