  Light field (LF) images containing information for multiple views have
numerous applications, which can be severely affected by low-light imaging.
Recent learning-based methods for low-light enhancement have some
disadvantages, such as a lack of noise suppression, complex training process
and poor performance in extremely low-light conditions. To tackle 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 intermediate tasks within a single network, including
denoising, luminance adjustment, refinement and detail enhancement, achieving
progressive restoration from small scale to full scale. Moreover, we design an
angular transformer block with an efficient view-token scheme to model the
global angular dependencies, and a multi-scale spatial transformer block to
encode the multi-scale local and global information within each view. To
address the issue of insufficient training data, we formulate a synthesis
pipeline by simulating the major noise sources with the estimated noise
parameters of LF camera. Experimental results demonstrate that our method
achieves the state-of-the-art performance on low-light LF restoration with high
efficiency.
