# Boosting Visual Recognition in Real-world Degradations via Unsupervised Feature Enhancement Module with Deep Channel Prior

arXiv: https://arxiv.org/abs/2404.01703 · HTML: https://mlsanity.com/paper/2404.01703/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 78 references that are in the map.
- First posted on arXiv 2 Apr 2024.

## Builds on (most cited first, 30 of 37)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://mlsanity.com/paper/1903.12261/) (2019)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [Learning Transferable Features with Deep Adaptation Networks](https://mlsanity.com/paper/1502.02791/) (2015)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [Deep Clustering for Unsupervised Learning of Visual Features](https://mlsanity.com/paper/1807.05520/) (2018)
- [Maximum Classifier Discrepancy for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1712.02560/) (2017)
- [Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement](https://mlsanity.com/paper/2001.06826/) (2020)
- [EnlightenGAN: Deep Light Enhancement without Paired Supervision](https://mlsanity.com/paper/1906.06972/) (2019)
- [Benchmarking Single Image Dehazing and Beyond](https://mlsanity.com/paper/1712.04143/) (2017)
- [Learning to Enhance Low-Light Image via Zero-Reference Deep Curve Estimation](https://mlsanity.com/paper/2103.00860/) (2021)
- [Deblurring by Realistic Blurring](https://mlsanity.com/paper/2004.01860/) (2020)
- [Getting to Know Low-light Images with The Exclusively Dark Dataset](https://mlsanity.com/paper/1805.11227/) (2018)
- [Dark Model Adaptation: Semantic Image Segmentation from Daytime to Nighttime](https://mlsanity.com/paper/1810.02575/) (2018)
- [EdgeNeXt: Efficiently Amalgamated CNN-Transformer Architecture for Mobile Vision Applications](https://mlsanity.com/paper/2206.10589/) (2022)
- [Understanding How Image Quality Affects Deep Neural Networks](https://mlsanity.com/paper/1604.04004/) (2016)
- [Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives](https://mlsanity.com/paper/2303.09824/) (2023)
- [Implicit Neural Representation for Cooperative Low-light Image Enhancement](https://mlsanity.com/paper/2303.11722/) (2023)
- [Curricular Contrastive Regularization for Physics-aware Single Image Dehazing](https://mlsanity.com/paper/2303.14218/) (2023)
- [Cross-Domain Gradient Discrepancy Minimization for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2106.04151/) (2021)
- [You Only Look Yourself: Unsupervised and Untrained Single Image Dehazing Neural Network](https://mlsanity.com/paper/2006.16829/) (2020)
- [CVF-SID: Cyclic multi-Variate Function for Self-Supervised Image Denoising by Disentangling Noise from Image](https://mlsanity.com/paper/2203.13009/) (2022)
- [URIE: Universal Image Enhancement for Visual Recognition in the Wild](https://mlsanity.com/paper/2007.08979/) (2020)
- [Optimal Transport for Unsupervised Denoising Learning](https://mlsanity.com/paper/2108.02574/) (2021)
- [Empowering Low-Light Image Enhancer through Customized Learnable Priors](https://mlsanity.com/paper/2309.01958/) (2023)

## Built on by (most cited first, 5 of 5)

- [Beyond conventional vision: RGB-event fusion for robust object detection in dynamic traffic scenarios](https://mlsanity.com/paper/2508.10704/) (2025)
- [Bidirectional Image-Event Guided Fusion Framework for Low-Light Image Enhancement](https://mlsanity.com/paper/2506.06120/) (2025)
- [Focus Through Motion: RGB-Event Collaborative Token Sparsification for Efficient Object Detection](https://mlsanity.com/paper/2509.03872/) (2025)
- [Remote Sensing Image Dehazing: A Systematic Review of Progress, Challenges, and Prospects](https://mlsanity.com/paper/2603.20289/) (2026)
- [Geometry-Aware Representation Denoising for Robust Multi-view 3D Reconstruction](https://mlsanity.com/paper/2605.26230/) (2026)

## Abstract (arXiv)

The environmental perception of autonomous vehicles in normal conditions have achieved considerable success in the past decade. However, various unfavourable conditions such as fog, low-light, and motion blur will degrade image quality and pose tremendous threats to the safety of autonomous driving. That is, when applied to degraded images, state-of-the-art visual models often suffer performance decline due to the feature content loss and artifact interference caused by statistical and structural properties disruption of captured images. To address this problem, this work proposes a novel Deep Channel Prior (DCP) for degraded visual recognition. Specifically, we observe that, in the deep representation space of pre-trained models, the channel correlations of degraded features with the same degradation type have uniform distribution even if they have different content and semantics, which can facilitate the mapping relationship learning between degraded and clear representations in high-sparsity feature space. Based on this, a novel plug-and-play Unsupervised Feature Enhancement Module (UFEM) is proposed to achieve unsupervised feature correction, where the multi-adversarial mechanism is introduced in the first stage of UFEM to achieve the latent content restoration and artifact removal in high-sparsity feature space. Then, the generated features are transferred to the second stage for global correlation modulation under the guidance of DCP to obtain high-quality and recognition-friendly features. Evaluations of three tasks and eight benchmark datasets demonstrate that our proposed method can comprehensively improve the performance of pre-trained models in real degradation conditions. The source code is available at https://github.com/liyuhang166/Deep_Channel_Prior