Under low-light environment, handheld photography suffers from severe camera shake under long exposure settings. Although existing deblurring algorithms have shown promising performance on well-exposed blurry images, they still cannot cope with low-light snapshots. Sophisticated noise and saturation regions are two dominating challenges in practical low-light deblurring. In this work, we propose a novel non-blind deblurring method dubbed image and feature space Wiener deconvolution network (INFWIDE) to tackle these problems systematically. In terms of algorithm design, INFWIDE proposes a two-branch architecture, which explicitly removes noise and hallucinates saturated regions in the image space and suppresses ringing artifacts in the feature space, and integrates the two complementary outputs with a subtle multi-scale fusion network for high quality night photograph deblurring. For effective network training, we design a set of loss functions integrating a forward imaging model and backward reconstruction to form a close-loop regularization to secure good convergence of the deep neural network. Further, to optimize INFWIDE's applicability in real low-light conditions, a physical-process-based low-light noise model is employed to synthesize realistic noisy night photographs for model training. Taking advantage of the traditional Wiener deconvolution algorithm's physically driven characteristics and arisen deep neural network's representation ability, INFWIDE can recover fine details while suppressing the unpleasant artifacts during deblurring. Extensive experiments on synthetic data and real data demonstrate the superior performance of the proposed approach.

翻译：在低光照环境下，手持摄影在长曝光设置下会遭受严重的相机抖动。现有去模糊算法虽在曝光良好的模糊图像上表现出色，但难以处理低光照快照。复杂噪声和饱和区域是实际低光照去模糊面临的两大主要挑战。本文提出一种新颖的非盲去模糊方法——图像与特征空间维纳反卷积网络（INFWIDE），系统性地解决这些问题。在算法设计上，INFWIDE采用双分支架构：图像空间分支显式去除噪声并修复饱和区域，特征空间分支抑制振铃伪影，并通过精细的多尺度融合网络将两者互补输出整合，实现高质量夜景照片去模糊。为实现有效网络训练，我们设计了一组损失函数，融合前向成像模型与反向重建形成闭环正则化，确保深度神经网络稳定收敛。此外，为优化INFWIDE在真实低光照环境中的适用性，采用基于物理过程的低光照噪声模型合成逼真的含噪夜景照片用于模型训练。通过融合传统维纳反卷积算法的物理驱动特性与深度神经网络的表征能力，INFWIDE能在去模糊过程中恢复精细细节并抑制不良伪影。在合成数据与真实数据上的广泛实验证明了所提方法的卓越性能。