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.
翻译:多视图属性的光场图像有许多应用,这些应用会受到低光成像的严重影响。最近以学习为基础的低光增强方法本身也有其缺点,例如没有噪音抑制、复杂的培训过程和极低光条件下的性能差。在充分利用多视图信息的同时,我们为解决这些缺陷提出一个高效的低光恢复变异器(LRT),供LF图像使用,由多个头执行特定的中间任务,包括拆落、亮度调整、精细度和细节增强,在一个单一网络内实现从小规模逐步恢复到全面规模。我们设计了一个具有视觉图案的角变异器块,以高效地模拟全球角关系,并设计一个多尺度的基于窗口的变异器块,以编码多尺度的当地和全球空间信息。为解决培训数据不足的问题,我们通过用估计的低光度摄影机噪音参数模拟主要噪音,来制定一个合成管道。实验结果表明,我们的方法可以在恢复极低光度和振动的低频图像方面取得优异性。