Binarized Low-light Raw Video Enhancement

arXiv:2403.19944v1 Announce Type: new
Abstract: Recently, deep neural networks have achieved excellent performance on low-light raw video enhancement. However, they often come with high computational complexity and large memory costs, which hinder their applications on resource-limited devices. In this paper, we explore the feasibility of applying the extremely compact binary neural network (BNN) to low-light raw video enhancement. Nevertheless, there are two main issues with binarizing video enhancement models. One is how to fuse the temporal information to improve low-light …

abstract applications arxiv binary compact complexity computational costs cs.cv devices eess.iv explore hinder however light low memory network networks neural network neural networks paper performance raw type video