# An Extensible and Lightweight Unified Architecture for Demosaicing Pixel-bin Image Sensors

arXiv: https://arxiv.org/abs/2606.13136 · HTML: https://mlsanity.com/paper/2606.13136/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 25 references that are in the map.
- First posted on arXiv 11 Jun 2026.

## Builds on (most cited first, 10 of 10)

- [Unprocessing Images for Learned Raw Denoising](https://mlsanity.com/paper/1811.11127/) (2018)
- [ARNIQA: Learning Distortion Manifold for Image Quality Assessment](https://mlsanity.com/paper/2310.14918/) (2023)
- [On weight initialization in deep neural networks](https://mlsanity.com/paper/1704.08863/) (2017)
- [Beyond Joint Demosaicking and Denoising: An Image Processing Pipeline for a Pixel-bin Image Sensor](https://mlsanity.com/paper/2104.09398/) (2021)
- [SAGAN: Adversarial Spatial-asymmetric Attention for Noisy Nona-Bayer Reconstruction](https://mlsanity.com/paper/2110.08619/) (2021)
- [MIPI 2022 Challenge on Quad-Bayer Re-mosaic: Dataset and Report](https://mlsanity.com/paper/2209.07060/) (2022)
- [Efficient Unified Demosaicing for Bayer and Non-Bayer Patterned Image Sensors](https://mlsanity.com/paper/2307.10667/) (2023)
- [Examining Joint Demosaicing and Denoising for Single-, Quad-, and Nona-Bayer Patterns](https://mlsanity.com/paper/2504.07145/) (2025)
- [PyNET-QxQ: An Efficient PyNET Variant for QxQ Bayer Pattern Demosaicing in CMOS Image Sensors](https://mlsanity.com/paper/2203.04314/) (2022)
- [ISP meets Deep Learning: A Survey on Deep Learning Methods for Image Signal Processing](https://mlsanity.com/paper/2305.11994/) (2023)

## Built on by (most cited first, 2 of 2)

- [SegDem: Segmentation helps Demosaicing](https://mlsanity.com/paper/2608.07916/) (2026)
- [Structural Guidance for Unified Joint Demosaicing and Denoising](https://mlsanity.com/paper/2608.09995/) (2026)

## Abstract (arXiv)

Pixel-bin image sensors are becoming the default choice for smartphone cameras due to their resolution vs light-gathering trade-off. However, their larger inter-color separation compared to the Bayer color filter array (CFA) makes them challenging to demosaic. Furthermore, existing deep learning-based demosaicing methods are CFA-specific, requiring multiple individual models that take up precious onboard resources and demand larger development and maintenance efforts. In this work, we propose a modular unified architecture for demosaicing various pixel-bin sensors that provides higher image quality while being extensible and lightweight. Additionally, to enable plug-and-play operation, we introduce a learning-free CFA-identification module to detect the CFA type of raw data accurately.