MIPI 2022 Challenge on RGBW Sensor Re-mosaic: Dataset and Report
MIPI 2022 Challenge on RGBW Sensor Re-mosaic: Dataset and Report
复制标题
MIPI 2022 RGBW 传感器重新马赛克挑战：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
随着移动平台对计算摄影和成像的需求不断增加，在相机系统中开发和集成具有新算法的先进图像传感器是普遍的。然而，缺乏高质量的研究数据和难得的产业界和学术界深入交流意见的机会，制约了移动智能摄影与成像（MIPI）的发展。为了弥补差距，我们引入了第一个MIPI挑战，包括五个专注于新型图像传感器和成像算法的轨道。本文介绍了五种轨道之一的RGBW联合Remosaic和Denoise，用于全分辨率的RGBW CFA到Bayer的插值。为参与者提供了一个新的数据集，其中包括70个（训练）和15个（验证）高质量RGBW和Bayer对的场景。此外，对于每个场景，分别在0dB、24dB和42dB处提供了不同噪声等级的RGBW。所有数据都是在室外和室内条件下使用RGBW传感器捕获的。使用客观指标评估最终结果，包括PSNR， SSIM， LPIPS和KLD。本文提供了在该挑战中开发的所有模型的详细描述。关于这个挑战的更多细节和数据集的链接可以在https://github.com/mipi-challenge/MIPI2022上找到。
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, RGBW Joint Remosaic and Denoise, one of the five tracks, working on the interpolation of RGBW CFA to Bayer at full resolution, is introduced. The participants were provided with a new dataset including 70 (training) and 15 (validation) scenes of high-quality RGBW and Bayer pairs. In addition, for each scene, RGBW of different noise levels was provided at 0dB, 24dB, and 42dB. All the data were captured using an RGBW 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.