This paper reviews the NTIRE 2024 RAW Image Super-Resolution Challenge,
highlighting the proposed solutions and results. New methods for RAW
Super-Resolution could be essential in modern Image Signal Processing (ISP)
pipelines, however, this problem is not as explored as in the RGB domain. Th
goal of this challenge is to upscale RAW Bayer images by 2x, considering
unknown degradations such as noise and blur. In the challenge, a total of 230
participants registered, and 45 submitted results during thee challenge period.
The performance of the top-5 submissions is reviewed and provided here as a
gauge for the current state-of-the-art in RAW Image Super-Resolution.

对 NTIRE 2024 原始图像超分辨率挑战赛及其结果进行综述，该挑战的目标是通过 2 倍放大 RAW Bayer 图像来探索现代图像信号处理中 RAW 超分辨率的新方法。其中，参与者注册了 230 个，挑战期间提交了 45 个结果，笔者对前 5 名的表现进行了回顾，作为 RAW 图像超分辨率领域现有技术水平的指标。

深度 RAW 图像超分辨率。NTIRE 2024 挑战调查

Deep RAW Image Super-Resolution. A NTIRE 2024 Challenge Survey

We consider the problem of 3D object pose estimation. While much recent work
has focused on the RGB domain, the reliance on accurately annotated images
limits their generalizability and scalability. On the other hand, the easily
available CAD models of objects are rich sources of data, providing a large
number of synthetically rendered images. In this paper, we solve this key
problem of existing methods requiring expensive 3D pose annotations by
proposing a new method that matches RGB images to CAD models for object pose
estimation. Our key innovations compared to existing work include removing the
need for either real-world textures for CAD models or explicit 3D pose
annotations for RGB images. We achieve this through a series of objectives that
learn how to select keypoints and enforce viewpoint and modality invariance
across RGB images and CAD model renderings. We conduct extensive experiments to
demonstrate that the proposed method can reliably estimate object pose in RGB
images, as well as generalize to object instances not seen during training.

本文介绍了一种用于三维目标姿态估计的新方法，将 RGB 图像与 CAD 模型匹配以实现目标姿态估计，通过学习选择关键点并保持视角和模态不变性的目标，消除了对昂贵的三维姿态注释的需求，并通过大量实验证明了该方法能够可靠地估计 RGB 图像中的目标姿态以及推广到训练期间未看到的目标实例。