MIPI 2022 Challenge on Quad-Bayer Re-mosaic: Dataset and Report
MIPI 2022 Challenge on Quad-Bayer Re-mosaic: Dataset and Report
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MIPI 2022 Quad-Bayer Re-mosaic 挑战赛：数据集和报告
DOI：
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Jinwei Gu
中科院分区：
文献类型：
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作者：
Qingyu Yang;Guang Yang;Jun Jiang;Chongyi Li;Ruicheng Feng;Shangchen Zhou;Wenxiu Sun;Qingpeng Zhu;Chen Change Loy;Jinwei Gu
随着移动平台上计算摄影和成像的需求不断增长，在相机系统中开发和集成先进的图像传感器与新颖的算法变得越来越普遍。然而，高质量研究数据的缺乏以及业界和学术界深入交流观点的机会难得，制约了移动智能摄影与成像（MIPI）的发展。为了弥补这一差距，我们推出了第一个 MIPI 挑战赛，其中包括专注于新型图像传感器和成像算法的五个赛道。本文介绍了Quad Joint Remosaic and Denoise，这是五个轨道之一，致力于以全分辨率将Quad CFA 插值到Bayer。为参与者提供了一个新的数据集，其中包括高质量 Quad 和 Bayer 对的 70 个（训练）和 15 个（验证）场景。此外，对于每个场景，提供了0dB、24dB和42dB不同噪声水平的Quad。所有数据都是在室外和室内条件下使用四传感器捕获的。最终结果使用客观指标进行评估，包括 PSNR、SSIM、LPIPS 和 KLD。本文详细描述了本次挑战赛中开发的所有模型。有关此挑战的更多详细信息以及数据集的链接，请访问 https://github.com/mipi-challenge/MIPI2022。
Developing and integrating advanced image sensors with novel algorithms in camera systems are prevalent with the increasing demand for computational photography and imaging on mobile platforms. However, the lack of high-quality data for research and the rare opportunity for in-depth exchange of views from industry and academia constrain the development of mobile intelligent photography and imaging (MIPI). To bridge the gap, we introduce the first MIPI challenge, including five tracks focusing on novel image sensors and imaging algorithms. In this paper, Quad Joint Remosaic and Denoise, one of the five tracks, working on the interpolation of Quad CFA to Bayer at full resolution, is introduced. The participants were provided a new dataset, including 70 (training) and 15 (validation) scenes of high-quality Quad and Bayer pairs. In addition, for each scene, Quad of different noise levels was provided at 0dB, 24dB, and 42dB. All the data were captured using a Quad sensor in both outdoor and indoor conditions. The final results are evaluated using objective metrics, including PSNR, SSIM, LPIPS, and KLD. A detailed description of all models developed in this challenge is provided in this paper. More details of this challenge and the link to the dataset can be found at https://github.com/mipi-challenge/MIPI2022.