JaeHyun Baek

TL;DR: The NTIRE 2020 challenge on perceptual extreme super-resolution as mentioned in this paper focused on super-resolving an input image with a magnification factor ×16 based on a set of prior examples of low and corresponding high resolution images.

TL;DR: This paper reviews the second AIM learned ISP challenge and provides the description of the proposed solutions and results, defining the state-of-the-art for practical image signal processing pipeline modeling.

TL;DR: The second AIM learned ISP challenge as mentioned in this paper focused on real-world RAW-to-RGB mapping problem, where the 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.

TL;DR: PyNET-CA as discussed by the authors is an end-to-end mobile ISP deep learning algorithm for RAW to RGB reconstruction, which enhances PyNET, a recently proposed state-of-the-art model for mobile ISP, and improves its performance with channel attention and subpixel reconstruction module.

TL;DR: Wang et al. as discussed by the authors proposed PyNET-CA, an end-to-end mobile ISP deep learning algorithm for RAW to RGB reconstruction, which enhances PyNET, a recently proposed state-of-the-art model for mobile ISP.