Video super-resolution (VSR) techniques, especially deep-learning-based
algorithms, have drastically improved over the last few years and shown
impressive performance on synthetic data. However, their performance on
real-world video data suffers because of the complexity of real-world
degradations and misaligned video frames. Since obtaining a synthetic dataset
consisting of low-resolution (LR) and high-resolution (HR) frames are easier
than obtaining real-world LR and HR images, in this paper, we propose
synthesizing real-world degradations on synthetic training datasets. The
proposed synthetic real-world degradations (SRWD) include a combination of the
blur, noise, downsampling, pixel binning, and image and video compression
artifacts. We then propose using a random shuffling-based strategy to simulate
these degradations on the training datasets and train a single end-to-end deep
neural network (DNN) on the proposed larger variation of realistic synthesized
training data. Our quantitative and qualitative comparative analysis shows that
the proposed training strategy using diverse realistic degradations improves
the performance by 7.1 % in terms of NRQM compared to RealBasicVSR and by 3.34
% compared to BSRGAN on the VideoLQ dataset. We also introduce a new dataset
that contains high-resolution real-world videos that can serve as a common
ground for bench-marking.

该论文提出了一种随机洗牌方法来模拟真实的降级因素并在合成的数据集上训练深度神经网络，使其在真实世界的视频超分辨率方面表现更好。结果显示，相较于 RealBasicVSR 在 NRQM 方面提高了 7.1％，相较于 BSRGAN 提高了 3.34％。同时，该论文也提出了一个新的包含高分辨率真实视频的数据集，可作为基准测试的共同参照。

扩展合成真实世界退化，用于盲视频超分辨率

Expanding Synthetic Real-World Degradations for Blind Video Super  Resolution

Deep-learning-based local feature extraction algorithms that combine
detection and description have made significant progress in visible image
matching. However, the end-to-end training of such frameworks is notoriously
unstable due to the lack of strong supervision of detection and the
inappropriate coupling between detection and description. The problem is
magnified in cross-modal scenarios, in which most methods heavily rely on the
pre-training. In this paper, we recouple independent constraints of detection
and description of multimodal feature learning with a mutual weighting
strategy, in which the detected probabilities of robust features are forced to
peak and repeat, while features with high detection scores are emphasized
during optimization. Different from previous works, those weights are detached
from back propagation so that the detected probability of indistinct features
would not be directly suppressed and the training would be more stable.
Moreover, we propose the Super Detector, a detector that possesses a large
receptive field and is equipped with learnable non-maximum suppression layers,
to fulfill the harsh terms of detection. Finally, we build a benchmark that
contains cross visible, infrared, near-infrared and synthetic aperture radar
image pairs for evaluating the performance of features in feature matching and
image registration tasks. Extensive experiments demonstrate that features
trained with the recoulped detection and description, named ReDFeat, surpass
previous state-of-the-arts in the benchmark, while the model can be readily
trained from scratch.

本文利用互相关联的检测和描述特点的相互加权策略进行多模态特征学习，提出了具有大触发范围的 Super Detector 和 learnable 非最大抑制层的 detector，并提出了可评估特征匹配和图像注册任务性能的基准系统，实验证明本文的方法可以显著提高匹配的准确性及训练的稳定性。

ReDFeat: 多模态特征学习中的重耦合检测和描述

ReDFeat: Recoupling Detection and Description for Multimodal Feature Learning

This paper presents an overview of the evolution of local features from
handcrafted to deep-learning-based methods, followed by a discussion of several
benchmarks and papers evaluating such local features. Our investigations are
motivated by 3D reconstruction problems, where the precise location of the
features is important. As we describe these methods, we highlight and explain
the challenges of feature extraction and potential ways to overcome them. We
first present handcrafted methods, followed by methods based on classical
machine learning and finally we discuss methods based on deep-learning. This
largely chronologically-ordered presentation will help the reader to fully
understand the topic of image and region description in order to make best use
of it in modern computer vision applications. In particular, understanding
handcrafted methods and their motivation can help to understand modern
approaches and how machine learning is used to improve the results. We also
provide references to most of the relevant literature and code.

该论文介绍了本地特征从手工制作到基于深度学习的方法的演变概述，讨论了评估这些本地特征的若干基准和论文，探讨了特征提取的挑战和潜在解决方案，并提供了相关文献和代码参考。