Snapshot HDR Video Construction Using Coded Mask

TL;DR

This paper presents a method using 3D convolutional neural networks to reconstruct high-quality, temporally consistent HDR videos from snapshot-coded LDR videos, enabling affordable HDR video capture with standard cameras.

Contribution

It introduces a novel deep learning approach with a temporal loss for improved HDR video reconstruction from coded LDR videos.

Findings

High-quality HDR video can be reconstructed from coded LDR videos.

Temporal loss improves consistency between frames.

Method is promising for affordable HDR video capture.

Abstract

This paper study the reconstruction of High Dynamic Range (HDR) video from snapshot-coded LDR video. Constructing an HDR video requires restoring the HDR values for each frame and maintaining the consistency between successive frames. HDR image acquisition from single image capture, also known as snapshot HDR imaging, can be achieved in several ways. For example, the reconfigurable snapshot HDR camera is realized by introducing an optical element into the optical stack of the camera; by placing a coded mask at a small standoff distance in front of the sensor. High-quality HDR image can be recovered from the captured coded image using deep learning methods. This study utilizes 3D-CNNs to perform a joint demosaicking, denoising, and HDR video reconstruction from coded LDR video. We enforce more temporally consistent HDR video reconstruction by introducing a temporal loss function that considers the short-term and long-term consistency. The obtained results are promising and could lead to affordable HDR video capture using conventional cameras.