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 Fusion and Denoise, one of the five tracks, working on the fusion of binning-mode RGBW to Bayer, 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 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.
翻译:由于对计算摄影和移动平台成像的需求不断增加,开发和整合先进的图像传感器和摄像系统的新算法十分普遍;然而,缺乏高质量的研究数据,而且行业和学术界深入交流观点的机会很少,限制了移动智能摄影和成像(MIPI)的发展;为了缩小差距,我们引入了第一个MIPI挑战,包括五个侧重于新图像传感器和成像算法的轨道,包括五个侧重于新图像传感器和成像算法的轨道:在本文中,五个轨道之一RGBW联合聚合和Denois,在将宾式-mode RGBW并入Bayer的工作中引入了五个轨道之一;向与会者提供了一套新的数据集,包括70个(培训)和15个(验证)高质量的RGBW和Bayer对景区(校验),从而限制了移动智能摄影和成像(MI)的开发。此外,每个场景,在24dB和42dB中都提供了不同噪声水平的RGBW。所有数据都是在户和室内条件下用RGBW传感器采集的。最后结果是用客观指标进行的,包括PSNRNR、SSIMN、LPISPIS和KIS和KMI。