AIM 2020 Challenge on Learned Image Signal Processing Pipeline
arXiv:2011.04994
Abstract
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.
Published in ECCV 2020 Workshops (Advances in Image Manipulation), https://data.vision.ee.ethz.ch/cvl/aim20/
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- PyNET-CA: Enhanced PyNET with Channel Attention for End-to-End Mobile Image Signal Processing
- Fast and Accurate Single-Image Depth Estimation on Mobile Devices, Mobile AI 2021 Challenge: Report
- Real-Time Quantized Image Super-Resolution on Mobile NPUs, Mobile AI 2021 Challenge: Report
- Del-Net: A Single-Stage Network for Mobile Camera ISP
- Fast Camera Image Denoising on Mobile GPUs with Deep Learning, Mobile AI 2021 Challenge: Report