Most of traditional single image deblurring methods before deep learning
adopt a coarse-to-fine scheme that estimates a sharp image at a coarse scale
and progressively refines it at finer scales. While this scheme has also been
adopted to several deep learning-based approaches, recently a number of
single-scale approaches have been introduced showing superior performance to
previous coarse-to-fine approaches both in quality and computation time. In
this paper, we revisit the coarse-to-fine scheme, and analyze defects of
previous coarse-to-fine approaches that degrade their performance. Based on the
analysis, we propose Multi-Scale-Stage Network (MSSNet), a novel deep
learning-based approach to single image deblurring that adopts our remedies to
the defects. Specifically, MSSNet adopts three novel technical components:
stage configuration reflecting blur scales, an inter-scale information
propagation scheme, and a pixel-shuffle-based multi-scale scheme. Our
experiments show that MSSNet achieves the state-of-the-art performance in terms
of quality, network size, and computation time.

本文研究单幅图像去模糊方法，使用深度学习技术设计了 Multi-Scale-Stage Network 方法，该方法通过三个创新技术组件来改进之前粗糙 - 精细方案，提高了去模糊质量、缩小了网络规模和加速了计算速度，并在评估中展现出最先进的性能。

MSSNet：用于单幅图像去模糊的多尺度级联网络

MSSNet: Multi-Scale-Stage Network for Single Image Deblurring

Multi-scale (MS) approaches have been widely investigated for blind single
image / video deblurring that sequentially recovers deblurred images in low
spatial scale first and then in high spatial scale later with the output of
lower scales. MS approaches have been effective especially for severe blurs
induced by large motions in high spatial scale since those can be seen as small
blurs in low spatial scale. In this work, we investigate alternative approach
to MS, called multi-temporal (MT) approach, for non-uniform single image
deblurring. We propose incremental temporal training with constructed MT level
dataset from time-resolved dataset, develop novel MT-RNNs with recurrent
feature maps, and investigate progressive single image deblurring over
iterations. Our proposed MT methods outperform state-of-the-art MS methods on
the GoPro dataset in PSNR with the smallest number of parameters.

本研究研究了多尺度和多时间尺度方法，提出一种新的 MT-RNNs 方法，用于逐步恢复非均匀模糊单幅图像。MT 方法在 GoPro 数据集上的表现优于现有的 MS 方法，且具有更少的参数。

用多时间点循环神经网络进行增量时间训练的非均匀单图像去模糊

Multi-Temporal Recurrent Neural Networks For Progressive Non-Uniform  Single Image Deblurring With Incremental Temporal Training

In single image deblurring, the "coarse-to-fine" scheme, i.e. gradually
restoring the sharp image on different resolutions in a pyramid, is very
successful in both traditional optimization-based methods and recent
neural-network-based approaches. In this paper, we investigate this strategy
and propose a Scale-recurrent Network (SRN-DeblurNet) for this deblurring task.
Compared with the many recent learning-based approaches in [25], it has a
simpler network structure, a smaller number of parameters and is easier to
train. We evaluate our method on large-scale deblurring datasets with complex
motion. Results show that our method can produce better quality results than
state-of-the-arts, both quantitatively and qualitatively.

本文介绍了一个称为 “SRN-DeblurNet” 的规模递归网络，该方法使用金字塔不同分辨率的 “粗到细” 方法逐步恢复锐化图像，在大规模模糊数据集上进行评估，结果显示我们的方法比现有方法在定量和定性上的呈现更好。