# MIPI 2022 Challenge on Quad-Bayer Re-mosaic: Dataset and Report

arXiv: https://arxiv.org/abs/2209.07060 · HTML: https://mlsanity.com/paper/2209.07060/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 16 references that are in the map.
- First posted on arXiv 15 Sep 2022.

## Builds on (most cited first, 9 of 9)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Image Super-Resolution Using Very Deep Residual Channel Attention Networks](https://mlsanity.com/paper/1807.02758/) (2018)
- [Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](https://mlsanity.com/paper/1609.05158/) (2016)
- [Uformer: A General U-Shaped Transformer for Image Restoration](https://mlsanity.com/paper/2106.03106/) (2021)
- [Plug-and-Play Image Restoration with Deep Denoiser Prior](https://mlsanity.com/paper/2008.13751/) (2020)
- [Learning Enriched Features for Real Image Restoration and Enhancement](https://mlsanity.com/paper/2003.06792/) (2020)
- [NTIRE 2020 Challenge on Real Image Denoising: Dataset, Methods and Results](https://mlsanity.com/paper/2005.04117/) (2020)
- [Hybrid Pixel-Unshuffled Network for Lightweight Image Super-Resolution](https://mlsanity.com/paper/2203.08921/) (2022)
- [PyNET-CA: Enhanced PyNET with Channel Attention for End-to-End Mobile Image Signal Processing](https://mlsanity.com/paper/2104.02895/) (2021)

## Built on by (most cited first, 9 of 9)

- [Binarized Mamba-Transformer for Lightweight Quad Bayer HybridEVS Demosaicing](https://mlsanity.com/paper/2503.16134/) (2025)
- [MIPI 2023 Challenge on Nighttime Flare Removal: Methods and Results](https://mlsanity.com/paper/2305.13770/) (2023)
- [Efficient Unified Demosaicing for Bayer and Non-Bayer Patterned Image Sensors](https://mlsanity.com/paper/2307.10667/) (2023)
- [MIPI 2024 Challenge on Demosaic for HybridEVS Camera: Methods and Results](https://mlsanity.com/paper/2405.04867/) (2024)
- [MIPI 2023 Challenge on RGBW Remosaic: Methods and Results](https://mlsanity.com/paper/2304.10551/) (2023)
- [Inheriting Bayer's Legacy-Joint Remosaicing and Denoising for Quad Bayer Image Sensor](https://mlsanity.com/paper/2303.13571/) (2023)
- [MIPI 2023 Challenge on RGBW Fusion: Methods and Results](https://mlsanity.com/paper/2304.10089/) (2023)
- [Lightweight Quad Bayer HybridEVS Demosaicing via State Space Augmented Cross-Attention](https://mlsanity.com/paper/2508.06058/) (2025)
- [An Extensible and Lightweight Unified Architecture for Demosaicing Pixel-bin Image Sensors](https://mlsanity.com/paper/2606.13136/) (2026)

## Abstract (arXiv)

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.