# Inheriting Bayer's Legacy-Joint Remosaicing and Denoising for Quad Bayer Image Sensor

arXiv: https://arxiv.org/abs/2303.13571 · HTML: https://mlsanity.com/paper/2303.13571/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 51 references that are in the map.
- First posted on arXiv 23 Mar 2023.

## Builds on (most cited first, 15 of 15)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement](https://mlsanity.com/paper/2001.06826/) (2020)
- [Multi-level Wavelet-CNN for Image Restoration](https://mlsanity.com/paper/1805.07071/) (2018)
- [DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks](https://mlsanity.com/paper/1704.02470/) (2017)
- [Practical Blind Image Denoising via Swin-Conv-UNet and Data Synthesis](https://mlsanity.com/paper/2203.13278/) (2022)
- [Abandoning the Bayer-Filter to See in the Dark](https://mlsanity.com/paper/2203.04042/) (2022)
- [Learning Raw Image Denoising with Bayer Pattern Unification and Bayer Preserving Augmentation](https://mlsanity.com/paper/1904.12945/) (2019)
- [Beyond Joint Demosaicking and Denoising: An Image Processing Pipeline for a Pixel-bin Image Sensor](https://mlsanity.com/paper/2104.09398/) (2021)
- [Joint Denoising and Demosaicking with Green Channel Prior for Real-world Burst Images](https://mlsanity.com/paper/2101.09870/) (2021)
- [SAGAN: Adversarial Spatial-asymmetric Attention for Noisy Nona-Bayer Reconstruction](https://mlsanity.com/paper/2110.08619/) (2021)
- [HighEr-Resolution Network for Image Demosaicing and Enhancing](https://mlsanity.com/paper/1911.08098/) (2019)
- [Transform your Smartphone into a DSLR Camera: Learning the ISP in the Wild](https://mlsanity.com/paper/2203.10636/) (2022)
- [MIPI 2022 Challenge on Quad-Bayer Re-mosaic: Dataset and Report](https://mlsanity.com/paper/2209.07060/) (2022)

## Built on by (most cited first, 4 of 4)

- [MambaSCI: Efficient Mamba-UNet for Quad-Bayer Patterned Video Snapshot Compressive Imaging](https://mlsanity.com/paper/2410.14214/) (2024)
- [Binarized Mamba-Transformer for Lightweight Quad Bayer HybridEVS Demosaicing](https://mlsanity.com/paper/2503.16134/) (2025)
- [Lightweight Quad Bayer HybridEVS Demosaicing via State Space Augmented Cross-Attention](https://mlsanity.com/paper/2508.06058/) (2025)
- [SegDem: Segmentation helps Demosaicing](https://mlsanity.com/paper/2608.07916/) (2026)

## Abstract (arXiv)

Pixel binning based Quad sensors have emerged as a promising solution to overcome the hardware limitations of compact cameras in low-light imaging. However, binning results in lower spatial resolution and non-Bayer CFA artifacts. To address these challenges, we propose a dual-head joint remosaicing and denoising network (DJRD), which enables the conversion of noisy Quad Bayer and standard noise-free Bayer pattern without any resolution loss. DJRD includes a newly designed Quad Bayer remosaicing (QB-Re) block, integrated denoising modules based on Swin-transformer and multi-scale wavelet transform. The QB-Re block constructs the convolution kernel based on the CFA pattern to achieve a periodic color distribution in the perceptual field, which is used to extract exact spectral information and reduce color misalignment. The integrated Swin-Transformer and multi-scale wavelet transform capture non-local dependencies, frequency and location information to effectively reduce practical noise. By identifying challenging patches utilizing Moire and zipper detection metrics, we enable our model to concentrate on difficult patches during the post-training phase, which enhances the model's performance in hard cases. Our proposed model outperforms competing models by approximately 3dB, without additional complexity in hardware or software.