# AWNet: Attentive Wavelet Network for Image ISP

arXiv: https://arxiv.org/abs/2008.09228 · HTML: https://mlsanity.com/paper/2008.09228/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Aug 2020.

## Built on by (most cited first, 30 of 38)

- [Learning RAW-to-sRGB Mappings with Inaccurately Aligned Supervision](https://mlsanity.com/paper/2108.08119/) (2021)
- [Learned Smartphone ISP on Mobile NPUs with Deep Learning, Mobile AI 2021 Challenge: Report](https://mlsanity.com/paper/2105.07809/) (2021)
- [ReconfigISP: Reconfigurable Camera Image Processing Pipeline](https://mlsanity.com/paper/2109.04760/) (2021)
- [Semi-Supervised Raw-to-Raw Mapping](https://mlsanity.com/paper/2106.13883/) (2021)
- [Raw or Cooked? Object Detection on RAW Images](https://mlsanity.com/paper/2301.08965/) (2023)
- [Enhancing RAW-to-sRGB with Decoupled Style Structure in Fourier Domain](https://mlsanity.com/paper/2401.02161/) (2024)
- [Learned Smartphone ISP on Mobile GPUs with Deep Learning, Mobile AI & AIM 2022 Challenge: Report](https://mlsanity.com/paper/2211.03885/) (2022)
- [MicroISP: Processing 32MP Photos on Mobile Devices with Deep Learning](https://mlsanity.com/paper/2211.06770/) (2022)
- [MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices](https://mlsanity.com/paper/2507.01838/) (2025)
- [Rawformer: Unpaired Raw-to-Raw Translation for Learnable Camera ISPs](https://mlsanity.com/paper/2404.10700/) (2024)
- [Transform your Smartphone into a DSLR Camera: Learning the ISP in the Wild](https://mlsanity.com/paper/2203.10636/) (2022)
- [Breaking Through the Haze: An Advanced Non-Homogeneous Dehazing Method based on Fast Fourier Convolution and ConvNeXt](https://mlsanity.com/paper/2305.04430/) (2023)
- [PyNet-V2 Mobile: Efficient On-Device Photo Processing With Neural Networks](https://mlsanity.com/paper/2211.06263/) (2022)
- [ISPDiffuser: Learning RAW-to-sRGB Mappings with Texture-Aware Diffusion Models and Histogram-Guided Color Consistency](https://mlsanity.com/paper/2503.19283/) (2025)
- [All You Need is RAW: Defending Against Adversarial Attacks with Camera Image Pipelines](https://mlsanity.com/paper/2112.09219/) (2021)
- [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)
- [MetaISP -- Exploiting Global Scene Structure for Accurate Multi-Device Color Rendition](https://mlsanity.com/paper/2401.03220/) (2024)
- [Overparametrization of HyperNetworks at Fixed FLOP-Count Enables Fast Neural Image Enhancement](https://mlsanity.com/paper/2105.08470/) (2021)
- [DRL-ISP: Multi-Objective Camera ISP with Deep Reinforcement Learning](https://mlsanity.com/paper/2207.03081/) (2022)
- [Del-Net: A Single-Stage Network for Mobile Camera ISP](https://mlsanity.com/paper/2108.01623/) (2021)
- [An End-to-End Real-World Camera Imaging Pipeline](https://mlsanity.com/paper/2411.10773/) (2024)
- [Learned Lightweight Smartphone ISP with Unpaired Data](https://mlsanity.com/paper/2505.10420/) (2025)
- [PQDynamicISP: Dynamically Controlled Image Signal Processor for Any Image Sensors Pursuing Perceptual Quality](https://mlsanity.com/paper/2403.10091/) (2024)
- [Simple Image Signal Processing using Global Context Guidance](https://mlsanity.com/paper/2404.11569/) (2024)
- [Window-based Channel Attention for Wavelet-enhanced Learned Image Compression](https://mlsanity.com/paper/2409.14090/) (2024)
- [CRISPnet: Color Rendition ISP Net](https://mlsanity.com/paper/2203.10562/) (2022)
- [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)
- [Experts-Guided Unbalanced Optimal Transport for ISP Learning from Unpaired and/or Paired Data](https://mlsanity.com/paper/2512.05635/) (2025)

## Abstract (arXiv)

As the revolutionary improvement being made on the performance of smartphones over the last decade, mobile photography becomes one of the most common practices among the majority of smartphone users. However, due to the limited size of camera sensors on phone, the photographed image is still visually distinct to the one taken by the digital single-lens reflex (DSLR) camera. To narrow this performance gap, one is to redesign the camera image signal processor (ISP) to improve the image quality. Owing to the rapid rise of deep learning, recent works resort to the deep convolutional neural network (CNN) to develop a sophisticated data-driven ISP that directly maps the phone-captured image to the DSLR-captured one. In this paper, we introduce a novel network that utilizes the attention mechanism and wavelet transform, dubbed AWNet, to tackle this learnable image ISP problem. By adding the wavelet transform, our proposed method enables us to restore favorable image details from RAW information and achieve a larger receptive field while remaining high efficiency in terms of computational cost. The global context block is adopted in our method to learn the non-local color mapping for the generation of appealing RGB images. More importantly, this block alleviates the influence of image misalignment occurred on the provided dataset. Experimental results indicate the advances of our design in both qualitative and quantitative measurements. The code is available publically.