# Deep Image Demosaicking using a Cascade of Convolutional Residual Denoising Networks

arXiv: https://arxiv.org/abs/1803.05215 · HTML: https://mlsanity.com/paper/1803.05215/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Mar 2018.

## Built on by (most cited first, 30 of 31)

- [Masked Image Training for Generalizable Deep Image Denoising](https://mlsanity.com/paper/2303.13132/) (2023)
- [Deep Reparametrization of Multi-Frame Super-Resolution and Denoising](https://mlsanity.com/paper/2108.08286/) (2021)
- [Memory-Augmented Deep Unfolding Network for Compressive Sensing](https://mlsanity.com/paper/2110.09766/) (2021)
- [Memory-Efficient Network for Large-scale Video Compressive Sensing](https://mlsanity.com/paper/2103.03089/) (2021)
- [Beyond Joint Demosaicking and Denoising: An Image Processing Pipeline for a Pixel-bin Image Sensor](https://mlsanity.com/paper/2104.09398/) (2021)
- [Progressive Joint Low-light Enhancement and Noise Removal for Raw Images](https://mlsanity.com/paper/2106.14844/) (2021)
- [Dynamic Path-Controllable Deep Unfolding Network for Compressive Sensing](https://mlsanity.com/paper/2306.16060/) (2023)
- [Image Demoireing in RAW and sRGB Domains](https://mlsanity.com/paper/2312.09063/) (2023)
- [SAGAN: Adversarial Spatial-asymmetric Attention for Noisy Nona-Bayer Reconstruction](https://mlsanity.com/paper/2110.08619/) (2021)
- [Optimization-Inspired Cross-Attention Transformer for Compressive Sensing](https://mlsanity.com/paper/2304.13986/) (2023)
- [PIDSR: Complementary Polarized Image Demosaicing and Super-Resolution](https://mlsanity.com/paper/2504.07758/) (2025)
- [Joint Multi-Scale Tone Mapping and Denoising for HDR Image Enhancement](https://mlsanity.com/paper/2303.09071/) (2023)
- [Deep Physics-Guided Unrolling Generalization for Compressed Sensing](https://mlsanity.com/paper/2307.08950/) (2023)
- [Efficient Unified Demosaicing for Bayer and Non-Bayer Patterned Image Sensors](https://mlsanity.com/paper/2307.10667/) (2023)
- [Joint Demosaicking and Denoising in the Wild: The Case of Training Under Ground Truth Uncertainty](https://mlsanity.com/paper/2101.04442/) (2021)
- [Fast Hierarchical Deep Unfolding Network for Image Compressed Sensing](https://mlsanity.com/paper/2208.01827/) (2022)
- [DRM-IR: Task-Adaptive Deep Unfolding Network for All-In-One Image Restoration](https://mlsanity.com/paper/2307.07688/) (2023)
- [Event Camera Demosaicing via Swin Transformer and Pixel-focus Loss](https://mlsanity.com/paper/2404.02731/) (2024)
- [NeurLZ: An Online Neural Learning-Based Method to Enhance Scientific Lossy Compression](https://mlsanity.com/paper/2409.05785/) (2024)
- [CDLNet: Noise-Adaptive Convolutional Dictionary Learning Network for Blind Denoising and Demosaicing](https://mlsanity.com/paper/2112.00913/) (2021)
- [PyNET-QxQ: An Efficient PyNET Variant for QxQ Bayer Pattern Demosaicing in CMOS Image Sensors](https://mlsanity.com/paper/2203.04314/) (2022)
- [Model-Guided Multi-Contrast Deep Unfolding Network for MRI Super-resolution Reconstruction](https://mlsanity.com/paper/2209.07030/) (2022)
- [Low Light Image Enhancement via Global and Local Context Modeling](https://mlsanity.com/paper/2101.00850/) (2021)
- [ISP meets Deep Learning: A Survey on Deep Learning Methods for Image Signal Processing](https://mlsanity.com/paper/2305.11994/) (2023)
- [Iterative Reweighted Least Squares Networks With Convergence Guarantees for Solving Inverse Imaging Problems](https://mlsanity.com/paper/2308.05745/) (2023)
- [SeKron: A Decomposition Method Supporting Many Factorization Structures](https://mlsanity.com/paper/2210.06299/) (2022)
- [SDAT: Sub-Dataset Alternation Training for Improved Image Demosaicing](https://mlsanity.com/paper/2303.15792/) (2023)
- [Toward Moir'e-Free and Detail-Preserving Demosaicking](https://mlsanity.com/paper/2305.08585/) (2023)
- [Frequency Enhancement for Image Demosaicking](https://mlsanity.com/paper/2503.15800/) (2025)
- [Color Matters: Demosaicing-Guided Color Correlation Training for Generalizable AI-Generated Image Detection](https://mlsanity.com/paper/2601.22778/) (2026)

## Abstract (arXiv)

Demosaicking and denoising are among the most crucial steps of modern digital camera pipelines and their joint treatment is a highly ill-posed inverse problem where at-least two-thirds of the information are missing and the rest are corrupted by noise. This poses a great challenge in obtaining meaningful reconstructions and a special care for the efficient treatment of the problem is required. While there are several machine learning approaches that have been recently introduced to deal with joint image demosaicking-denoising, in this work we propose a novel deep learning architecture which is inspired by powerful classical image regularization methods and large-scale convex optimization techniques. Consequently, our derived network is more transparent and has a clear interpretation compared to alternative competitive deep learning approaches. Our extensive experiments demonstrate that our network outperforms any previous approaches on both noisy and noise-free data. This improvement in reconstruction quality is attributed to the principled way we design our network architecture, which also requires fewer trainable parameters than the current state-of-the-art deep network solution. Finally, we show that our network has the ability to generalize well even when it is trained on small datasets, while keeping the overall number of trainable parameters low.