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 an in-depth exchange of views
from industry and academia constrain the development of mobile intelligent
photography and imaging (MIPI). With the success of the 1st MIPI Workshop@ECCV
2022, we introduce the second MIPI challenge, including four tracks focusing on
novel image sensors and imaging algorithms. This paper summarizes and reviews
the RGBW Joint Fusion and Denoise track on MIPI 2023. In total, 69 participants
were successfully registered, and 4 teams submitted results in the final
testing phase. The final results are evaluated using objective metrics,
including PSNR, SSIM, LPIPS, and KLD. A detailed description of the top three
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
this https URL

本文总结了 MIPI 2023 的 RGBW 联合融合和去噪声跟踪，对于其中排名前三的模型进行了详细描述，并使用 PSNR，SSIM，LPIPS 和 KLD 等客观指标进行了测评。

MIPI 2023 RGBW 融合挑战：方法和结果

MIPI 2023 Challenge on RGBW Fusion: Methods and Results

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 this https URL

本文介绍了 MIPI 挑战赛中一项 RGBW Joint Fusion 和去噪的任务，包括提供的数据集和使用的客观指标，这有助于促进移动智能摄影和图像领域的发展。