# HighEr-Resolution Network for Image Demosaicing and Enhancing

arXiv: https://arxiv.org/abs/1911.08098 · HTML: https://mlsanity.com/paper/1911.08098/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Nov 2019.

## Built on by (most cited first, 11 of 11)

- [Learning Enriched Features for Fast Image Restoration and Enhancement](https://mlsanity.com/paper/2205.01649/) (2022)
- [Optical Aberration Correction in Postprocessing using Imaging Simulation](https://mlsanity.com/paper/2305.05867/) (2023)
- [PyNET-CA: Enhanced PyNET with Channel Attention for End-to-End Mobile Image Signal Processing](https://mlsanity.com/paper/2104.02895/) (2021)
- [Computational Optics for Mobile Terminals in Mass Production](https://mlsanity.com/paper/2305.05886/) (2023)
- [SYENet: A Simple Yet Effective Network for Multiple Low-Level Vision Tasks with Real-time Performance on Mobile Device](https://mlsanity.com/paper/2308.08137/) (2023)
- [Inheriting Bayer's Legacy-Joint Remosaicing and Denoising for Quad Bayer Image Sensor](https://mlsanity.com/paper/2303.13571/) (2023)
- [SDAN: Squared Deformable Alignment Network for Learning Misaligned Optical Zoom](https://mlsanity.com/paper/2104.00848/) (2021)
- [Rendering Nighttime Image Via Cascaded Color and Brightness Compensation](https://mlsanity.com/paper/2204.08970/) (2022)
- [LW-ISP: A Lightweight Model with ISP and Deep Learning](https://mlsanity.com/paper/2210.03904/) (2022)
- [ISP meets Deep Learning: A Survey on Deep Learning Methods for Image Signal Processing](https://mlsanity.com/paper/2305.11994/) (2023)
- [Leveraging Motion Estimation for Efficient Bayer-Domain Video Convolutional Networks](https://mlsanity.com/paper/2501.15119/) (2025)

## Abstract (arXiv)

Neural-networks based image restoration methods tend to use low-resolution image patches for training. Although higher-resolution image patches can provide more global information, state-of-the-art methods cannot utilize them due to their huge GPU memory usage, as well as the instable training process. However, plenty of studies have shown that global information is crucial for image restoration tasks like image demosaicing and enhancing. In this work, we propose a HighEr-Resolution Network (HERN) to fully learning global information in high-resolution image patches. To achieve this, the HERN employs two parallel paths to learn image features in two different resolutions, respectively. By combining global-aware features and multi-scale features, our HERN is able to learn global information with feasible GPU memory usage. Besides, we introduce a progressive training method to solve the instability issue and accelerate model convergence. On the task of image demosaicing and enhancing, our HERN achieves state-of-the-art performance on the AIM2019 RAW to RGB mapping challenge. The source code of our implementation is available at https://github.com/MKFMIKU/RAW2RGBNet.