# Del-Net: A Single-Stage Network for Mobile Camera ISP

arXiv: https://arxiv.org/abs/2108.01623 · HTML: https://mlsanity.com/paper/2108.01623/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 20 references that are in the map.
- First posted on arXiv 3 Aug 2021.

## Builds on (most cited first, 12 of 12)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Image Super-Resolution Using Very Deep Residual Channel Attention Networks](https://mlsanity.com/paper/1807.02758/) (2018)
- [Learning Enriched Features for Real Image Restoration and Enhancement](https://mlsanity.com/paper/2003.06792/) (2020)
- [Unprocessing Images for Learned Raw Denoising](https://mlsanity.com/paper/1811.11127/) (2018)
- [Multi-level Wavelet-CNN for Image Restoration](https://mlsanity.com/paper/1805.07071/) (2018)
- [Real Image Denoising with Feature Attention](https://mlsanity.com/paper/1904.07396/) (2019)
- [Replacing Mobile Camera ISP with a Single Deep Learning Model](https://mlsanity.com/paper/2002.05509/) (2020)
- [AIM 2020 Challenge on Learned Image Signal Processing Pipeline](https://mlsanity.com/paper/2011.04994/) (2020)
- [CameraNet: A Two-Stage Framework for Effective Camera ISP Learning](https://mlsanity.com/paper/1908.01481/) (2019)
- [AWNet: Attentive Wavelet Network for Image ISP](https://mlsanity.com/paper/2008.09228/) (2020)

## Built on by (most cited first, 3 of 3)

- [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)
- [RPBA-Net: An Interpretable Residual Pyramid Bilateral Affine Network for RAW-Domain ISP Enhancement](https://mlsanity.com/paper/2605.03626/) (2026)

## Abstract (arXiv)

The quality of images captured by smartphones is an important specification since smartphones are becoming ubiquitous as primary capturing devices. The traditional image signal processing (ISP) pipeline in a smartphone camera consists of several image processing steps performed sequentially to reconstruct a high quality sRGB image from the raw sensor data. These steps consist of demosaicing, denoising, white balancing, gamma correction, colour enhancement, etc. Since each of them are performed sequentially using hand-crafted algorithms, the residual error from each processing module accumulates in the final reconstructed signal. Thus, the traditional ISP pipeline has limited reconstruction quality in terms of generalizability across different lighting conditions and associated noise levels while capturing the image. Deep learning methods using convolutional neural networks (CNN) have become popular in solving many image-related tasks such as image denoising, contrast enhancement, super resolution, deblurring, etc. Furthermore, recent approaches for the RAW to sRGB conversion using deep learning methods have also been published, however, their immense complexity in terms of their memory requirement and number of Mult-Adds make them unsuitable for mobile camera ISP. In this paper we propose DelNet - a single end-to-end deep learning model - to learn the entire ISP pipeline within reasonable complexity for smartphone deployment. Del-Net is a multi-scale architecture that uses spatial and channel attention to capture global features like colour, as well as a series of lightweight modified residual attention blocks to help with denoising. For validation, we provide results to show the proposed Del-Net achieves compelling reconstruction quality.