论文标题
LRT:暗光场图像有效的低光修复变压器
LRT: An Efficient Low-Light Restoration Transformer for Dark Light Field Images
论文作者
论文摘要
包含多个视图信息的光场(LF)图像具有多种应用,这些应用可能会受到低光成像的严重影响。近期基于学习的低光增强方法具有一些缺点,例如缺乏噪声抑制,复杂的训练过程和在极低光线下的性能差。为了解决这些缺陷,在充分利用多视图信息的同时,我们为LF图像提出了有效的低光修复变压器(LRT),多个头部在单个网络中执行中间任务,包括deNoing,亮度调整,改进,改进和细节增强,从小到全尺度到全尺度。此外,我们设计了一个具有高效的视角方案的角变压器块,以模拟全局角依赖性,并设计一个多尺度的空间变压器块,以编码每个视图中的多尺度本地和全局信息。为了解决训练数据不足的问题,我们通过使用LF摄像机的估计噪声参数模拟主要噪声源来制定合成管道。实验结果表明,我们的方法在低效率下实现了低光恢复的最新性能。
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.