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Found 5 papers, 1 papers with code
Solving Inverse Computational Imaging Problems using Deep Pixel-level Prior
On the other hand, a more flexible approach would be to learn a deep generative model once and then use it as a signal prior for solving various inverse problems.
Learning Light Field Reconstruction from a Single Coded Image
We propose to do this in three stages 1) reconstruction of center view from the coded image 2) estimating disparity map from the coded image and center view 3) warping center view using the disparity to generate light field.
Compressive Image Recovery Using Recurrent Generative Model
Reconstruction of signals from compressively sensed measurements is an ill-posed problem.
A Unified Learning Based Framework for Light Field Reconstruction from Coded Projections
Here, we present a unified learning framework that can reconstruct LF from a variety of multiplexing schemes with minimal number of coded images as input.
Optimal HDR and Depth from Dual Cameras
Finally, we present for the first time a stereo HDR dataset consisting of dense ISO and exposure stack captured from a smartphone dual camera.
