# Wavelength-based Attributed Deep Neural Network for Underwater Image Restoration

arXiv: https://arxiv.org/abs/2106.07910 · HTML: https://mlsanity.com/paper/2106.07910/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 41 of its 91 references that are in the map.
- First posted on arXiv 15 Jun 2021.

## Builds on (most cited first, 30 of 41)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [Image Super-Resolution Using Deep Convolutional Networks](https://mlsanity.com/paper/1501.00092/) (2014)
- [Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](https://mlsanity.com/paper/1609.05158/) (2016)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [Multi-Scale Context Aggregation by Dilated Convolutions](https://mlsanity.com/paper/1511.07122/) (2015)
- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [Accurate Image Super-Resolution Using Very Deep Convolutional Networks](https://mlsanity.com/paper/1511.04587/) (2015)
- [Residual Dense Network for Image Super-Resolution](https://mlsanity.com/paper/1802.08797/) (2018)
- [Convolutional Pose Machines](https://mlsanity.com/paper/1602.00134/) (2016)
- [An Underwater Image Enhancement Benchmark Dataset and Beyond](https://mlsanity.com/paper/1901.05495/) (2019)
- [Hand Keypoint Detection in Single Images using Multiview Bootstrapping](https://mlsanity.com/paper/1704.07809/) (2017)
- [Large Kernel Matters -- Improve Semantic Segmentation by Global Convolutional Network](https://mlsanity.com/paper/1703.02719/) (2017)
- [Multi-level Wavelet-CNN for Image Restoration](https://mlsanity.com/paper/1805.07071/) (2018)
- [Fast Underwater Image Enhancement for Improved Visual Perception](https://mlsanity.com/paper/1903.09766/) (2019)
- [Understanding Convolution for Semantic Segmentation](https://mlsanity.com/paper/1702.08502/) (2017)
- [Deep Learning for Single Image Super-Resolution: A Brief Review](https://mlsanity.com/paper/1808.03344/) (2018)
- [Compression Artifacts Reduction by a Deep Convolutional Network](https://mlsanity.com/paper/1504.06993/) (2015)
- [Underwater Image Enhancement via Medium Transmission-Guided Multi-Color Space Embedding](https://mlsanity.com/paper/2104.13015/) (2021)
- [Enhancing Underwater Imagery using Generative Adversarial Networks](https://mlsanity.com/paper/1801.04011/) (2018)
- [Semantic Segmentation of Underwater Imagery: Dataset and Benchmark](https://mlsanity.com/paper/2004.01241/) (2020)
- [Underwater Single Image Color Restoration Using Haze-Lines and a New Quantitative Dataset](https://mlsanity.com/paper/1811.01343/) (2018)

## Built on by (most cited first, 23 of 23)

- [Underwater Image Restoration Through a Prior Guided Hybrid Sense Approach and Extensive Benchmark Analysis](https://mlsanity.com/paper/2501.02701/) (2025)
- [Phaseformer: Phase-based Attention Mechanism for Underwater Image Restoration and Beyond](https://mlsanity.com/paper/2412.01456/) (2024)
- [MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices](https://mlsanity.com/paper/2507.01838/) (2025)
- [Improving Underwater Visual Tracking With a Large Scale Dataset and Image Enhancement](https://mlsanity.com/paper/2308.15816/) (2023)
- [A Comprehensive Survey on Underwater Image Enhancement Based on Deep Learning](https://mlsanity.com/paper/2405.19684/) (2024)
- [PHISWID: Physics-Inspired Underwater Image Dataset Synthesized from RGB-D Images](https://mlsanity.com/paper/2404.03998/) (2024)
- [Adaptive deep learning framework for robust unsupervised underwater image enhancement](https://mlsanity.com/paper/2212.08983/) (2022)
- [Underwater Image Enhancement by Convolutional Spiking Neural Networks](https://mlsanity.com/paper/2503.20485/) (2025)
- [UIE-UnFold: Deep Unfolding Network with Color Priors and Vision Transformer for Underwater Image Enhancement](https://mlsanity.com/paper/2408.10653/) (2024)
- [SINET: Sparsity-driven Interpretable Neural Network for Underwater Image Enhancement](https://mlsanity.com/paper/2409.01022/) (2024)
- [Towards Realistic Underwater Dataset Generation and Color Restoration](https://mlsanity.com/paper/2211.14821/) (2022)
- [IDA-UIE: An Iterative Framework for Deep Network-based Degradation Aware Underwater Image Enhancement](https://mlsanity.com/paper/2406.18628/) (2024)
- [SeagrassFinder: Deep Learning for Eelgrass Detection and Coverage Estimation in the Wild](https://mlsanity.com/paper/2412.16147/) (2024)
- [FUSION: Frequency-guided Underwater Spatial Image recOnstructioN](https://mlsanity.com/paper/2504.01243/) (2025)
- [Impact of Underwater Image Enhancement on Feature Matching](https://mlsanity.com/paper/2507.21715/) (2025)
- [Style-Decoupled Adaptive Routing Network for Underwater Image Enhancement](https://mlsanity.com/paper/2604.12257/) (2026)
- [Uni-Removal: A Semi-Supervised Framework for Simultaneously Addressing Multiple Degradations in Real-World Images](https://mlsanity.com/paper/2307.05075/) (2023)
- [Unrevealed Threats: A Comprehensive Study of the Adversarial Robustness of Underwater Image Enhancement Models](https://mlsanity.com/paper/2409.06420/) (2024)
- [snnTrans-DHZ: A Lightweight Spiking Neural Network Architecture for Underwater Image Dehazing](https://mlsanity.com/paper/2504.11482/) (2025)
- [WaterWave: Bridging Underwater Image Enhancement into Video Streams via Wavelet-based Temporal Consistency Field](https://mlsanity.com/paper/2512.05492/) (2025)
- [TIDE: Two-Stage Inverse Degradation Estimation with Guided Prior Disentanglement for Underwater Image Restoration](https://mlsanity.com/paper/2512.07171/) (2025)
- [WaterGen: Decoupling Scene and Medium in Underwater Image Generation](https://mlsanity.com/paper/2606.31147/) (2026)
- [Dynamic SpectraFormer for Ultra-High-Definition Underwater Image Enhancement](https://mlsanity.com/paper/2608.18662/) (2026)

## Abstract (arXiv)

Background: Underwater images, in general, suffer from low contrast and high color distortions due to the non-uniform attenuation of the light as it propagates through the water. In addition, the degree of attenuation varies with the wavelength resulting in the asymmetric traversing of colors. Despite the prolific works for underwater image restoration (UIR) using deep learning, the above asymmetricity has not been addressed in the respective network engineering. Contributions: As the first novelty, this paper shows that attributing the right receptive field size (context) based on the traversing range of the color channel may lead to a substantial performance gain for the task of UIR. Further, it is important to suppress the irrelevant multi-contextual features and increase the representational power of the model. Therefore, as a second novelty, we have incorporated an attentive skip mechanism to adaptively refine the learned multi-contextual features. The proposed framework, called Deep WaveNet, is optimized using the traditional pixel-wise and feature-based cost functions. An extensive set of experiments have been carried out to show the efficacy of the proposed scheme over existing best-published literature on benchmark datasets. More importantly, we have demonstrated a comprehensive validation of enhanced images across various high-level vision tasks, e.g., underwater image semantic segmentation, and diver's 2D pose estimation. A sample video to exhibit our real-world performance is available at urlhttps://tinyurl.com/yzcrup9n. Also, we have open-sourced our framework at urlhttps://github.com/pksvision/Deep-WaveNet-UnderwaterImage-Restoration.