The Light Field Camera: Extended Depth of Field, Aliasing, and Superresolution

Abstract

Portable light field (LF) cameras have demonstrated capabilities beyond conventional cameras. In a single snapshot, they enable digital image refocusing and 3D reconstruction. We show that they obtain a larger depth of field but maintain the ability to reconstruct detail at high resolution. In fact, all depths are approximately focused, except for a thin slab where blur size is bounded, i.e., their depth of field is essentially inverted compared to regular cameras. Crucial to their success is the way they sample the LF, trading off spatial versus angular resolution, and how aliasing affects the LF. We show that applying traditional multiview stereo methods to the extracted low-resolution views can result in reconstruction errors due to aliasing. We address these challenges using an explicit image formation model, and incorporate Lambertian and texture preserving priors to reconstruct both scene depth and its superresolved texture in a variational Bayesian framework, eliminating aliasing by fusing multiview information. We demonstrate the method on synthetic and real images captured with our LF camera, and show that it can outperform other computational camera systems.

Globally Consistent Multi-label Assignment on the Ray Space of 4D Light Fields

“…Note that according to (2), the full regularizer J λμ which is defined on 4D ray space decomposes into a sum of 2D regularizers on the epipolar plane images and individual views, respectively. While solving a single saddle point problem for the full regularizer would require too much memory, it is feasible to iteratively compute independent descent steps for the data term and regularizer components.…”

“…Recent developments in light field acquisition systems [2,18,19,20] strengthen the prediction that we might soon enter an age of light field photography [17]. Since compared to a single image, light fields increase the content captured of a scene by directional information, they require an adaption of established algorithms in image processing and computer vision as well as the development of completely novel techniques.…”

A practical method for measuring the spatial frequency response of light field cameras

“…By considering the resolution of each microlens independently, this method cannot estimate the periodical dip in resolution where information is lost due to the overlapping of light field samples [14]. To overcome this issue, the disparity between the slanted edge locations in neighboring microimages should be taken into account.…”

Epipolar Plane Image Rectification and Flat Surface Detection in Light Field

“…Different from 2D image captured by traditional camera, light field camera records extra angular information of the real world and it provides more possibilities for many traditional computer vision tasks [5][6][7][8].…”