arXiv Analytics

Sign in

arXiv:2209.02197 [cs.CV]AbstractReferencesReviewsResources

LRT: An Efficient Low-Light Restoration Transformer for Dark Light Field Images

Shansi Zhang, Nan Meng, Edmund Y. Lam

Published 2022-09-06Version 1

Light field (LF) images with the multi-view property have many applications, which can be severely affected by the low-light imaging. Recent learning-based methods for low-light enhancement have their own disadvantages, such as no noise suppression, complex training process and poor performance in extremely low-light conditions. Targeted on solving these deficiencies while fully utilizing the multi-view information, we propose an efficient Low-light Restoration Transformer (LRT) for LF images, with multiple heads to perform specific intermediate tasks, including denoising, luminance adjustment, refinement and detail enhancement, within a single network, achieving progressive restoration from small scale to full scale. We design an angular transformer block with a view-token scheme to model the global angular relationship efficiently, and a multi-scale window-based transformer block to encode the multi-scale local and global spatial information. To solve the problem of insufficient training data, we formulate a synthesis pipeline by simulating the major noise with the estimated noise parameters of LF camera. Experimental results demonstrate that our method can achieve superior performance on the restoration of extremely low-light and noisy LF images with high efficiency.

Related articles: Most relevant | Search more
arXiv:1912.04459 [cs.CV] (Published 2019-12-10)
DeOccNet: Learning to See Through Foreground Occlusions in Light Fields
arXiv:2301.06392 [cs.CV] (Published 2023-01-16)
I See-Through You: A Framework for Removing Foreground Occlusion in Both Sparse and Dense Light Field Images
arXiv:2104.06637 [cs.CV] (Published 2021-04-14)
Decoupled Spatial-Temporal Transformer for Video Inpainting
Rui Liu et al.