Images taken in dynamic scenes may contain unwanted motion blur, which
significantly degrades visual quality. Such blur causes short- and long-range
region-specific smoothing artifacts that are often directional and non-uniform,
which is difficult to be removed. Inspired by the current success of
transformers on computer vision and image processing tasks, we develop,
Stripformer, a transformer-based architecture that constructs intra- and
inter-strip tokens to reweight image features in the horizontal and vertical
directions to catch blurred patterns with different orientations. It stacks
interlaced intra-strip and inter-strip attention layers to reveal blur
magnitudes. In addition to detecting region-specific blurred patterns of
various orientations and magnitudes, Stripformer is also a token-efficient and
parameter-efficient transformer model, demanding much less memory usage and
computation cost than the vanilla transformer but works better without relying
on tremendous training data. Experimental results show that Stripformer
performs favorably against state-of-the-art models in dynamic scene deblurring.

本文介绍了一种基于 transformer 的深度学习架构 Stripformer，用于去除动态场景下照片中的运动模糊，相较于传统的 transformer 具有更高效的运行速度和更好的表现效果。

Stripformer：用于快速图像去模糊的条形变压器

Stripformer: Strip Transformer for Fast Image Deblurring

The goal of dynamic scene deblurring is to remove the motion blur in a given
image. Typical learning-based approaches implement their solutions by
minimizing the L1 or L2 distance between the output and the reference sharp
image. Recent attempts adopt visual recognition features in training to improve
the perceptual quality. However, those features are primarily designed to
capture high-level contexts rather than low-level structures such as
blurriness. Instead, we propose a more direct way to make images sharper by
exploiting the inverse task of deblurring, namely, reblurring. Reblurring
amplifies the remaining blur to rebuild the original blur, however, a
well-deblurred clean image with zero-magnitude blur is hard to reblur. Thus, we
design two types of reblurring loss functions for better deblurring. The
supervised reblurring loss at training stage compares the amplified blur
between the deblurred and the sharp images. The self-supervised reblurring loss
at inference stage inspects if there noticeable blur remains in the deblurred.
Our experimental results on large-scale benchmarks and real images demonstrate
the effectiveness of the reblurring losses in improving the perceptual quality
of the deblurred images in terms of NIQE and LPIPS scores as well as visual
sharpness.

该论文提出了一种通过利用反模糊的方法实现对动态场景的去模糊，使用两种类型的重新模糊损失函数进行更好的去模糊，并通过实验证明其有效性。

清晰图像难以模糊化：利用病态逆问题进行动态场景去模糊

Clean Images are Hard to Reblur: Exploiting the Ill-Posed Inverse Task  for Dynamic Scene Deblurring

Motion blur in dynamic scenes is an important yet challenging research topic.
Recently, deep learning methods have achieved impressive performance for
dynamic scene deblurring. However, the motion information contained in a blurry
image has yet to be fully explored and accurately formulated because: (i) the
ground truth of dynamic motion is difficult to obtain; (ii) the temporal
ordering is destroyed during the exposure; and (iii) the motion estimation from
a blurry image is highly ill-posed. By revisiting the principle of camera
exposure, motion blur can be described by the relative motions of sharp content
with respect to each exposed position. In this paper, we define exposure
trajectories, which represent the motion information contained in a blurry
image and explain the causes of motion blur. A novel motion offset estimation
framework is proposed to model pixel-wise displacements of the latent sharp
image at multiple timepoints. Under mild constraints, our method can recover
dense, (non-)linear exposure trajectories, which significantly reduce temporal
disorder and ill-posed problems. Finally, experiments demonstrate that the
recovered exposure trajectories not only capture accurate and interpretable
motion information from a blurry image, but also benefit motion-aware image
deblurring and warping-based video extraction tasks. Codes are available on
this https URL

该论文提出了一种基于相机曝光原理的运动模糊图像恢复方法，通过定义曝光轨迹和提出一种运动偏移估计框架，成功地恢复了包含运动信息的图像，并在图像去模糊和基于运动畸变的视频提取任务中取得了较好的性能。