MIPI 2022 Challenge on RGBW Sensor Fusion: Dataset and Report

TL;DR

This paper presents the first MIPI challenge focusing on RGBW sensor fusion, providing a new dataset and evaluating various algorithms for image fusion and denoising in mobile imaging.

Contribution

It introduces a novel dataset for RGBW and Bayer image pairs, and reports on the performance of different algorithms in the first MIPI challenge for RGBW sensor fusion.

Findings

Algorithms achieved varying levels of PSNR and SSIM performance.

The dataset enabled benchmarking of RGBW sensor fusion methods.

Objective metrics like LPIPS and KLD were used for evaluation.

Abstract

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.