This paper reviews the second AIM learned ISP challenge and provides the
description of the proposed solutions and results. The participating teams were
solving a real-world RAW-to-RGB mapping problem, where to goal was to map the
original low-quality RAW images captured by the Huawei P20 device to the same
photos obtained with the Canon 5D DSLR camera. The considered task embraced a
number of complex computer vision subtasks, such as image demosaicing,
denoising, white balancing, color and contrast correction, demoireing, etc. The
target metric used in this challenge combined fidelity scores (PSNR and SSIM)
with solutions' perceptual results measured in a user study. The proposed
solutions significantly improved the baseline results, defining the
state-of-the-art for practical image signal processing pipeline modeling.

本文介绍了第二届 AIM 学习的 ISP 挑战赛的参赛队伍根据所提出的解决方案和结果。参赛者必须将由华为 P20 手机拍摄的原始低质量 RAW 图像映射到使用佳能 5D DSLR 相机获得的相同照片，该任务涵盖了许多复杂的计算机视觉子任务，例如图像重采样，去噪，白平衡，颜色和对比度校正等。挑战使用精度分数（PSNR 和 SSIM）和用户研究测量的解决方案的感知结果对目标指标进行评估。提出的解决方案显着改善了基线结果，定义了实际图像信号处理流水线建模的最新技术成果。