# Connecting Image Denoising and High-Level Vision Tasks via Deep Learning

arXiv: https://arxiv.org/abs/1809.01826 · HTML: https://mlsanity.com/paper/1809.01826/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Sep 2018.

## Built on by (most cited first, 30 of 39)

- [Blind2Unblind: Self-Supervised Image Denoising with Visible Blind Spots](https://mlsanity.com/paper/2203.06967/) (2022)
- [Instance Segmentation in the Dark](https://mlsanity.com/paper/2304.14298/) (2023)
- [LG-BPN: Local and Global Blind-Patch Network for Self-Supervised Real-World Denoising](https://mlsanity.com/paper/2304.00534/) (2023)
- [An Interactively Reinforced Paradigm for Joint Infrared-Visible Image Fusion and Saliency Object Detection](https://mlsanity.com/paper/2305.09999/) (2023)
- [HazyDet: Open-Source Benchmark for Drone-View Object Detection with Depth-Cues in Hazy Scenes](https://mlsanity.com/paper/2409.19833/) (2024)
- [SAPNet: Segmentation-Aware Progressive Network for Perceptual Contrastive Deraining](https://mlsanity.com/paper/2111.08892/) (2021)
- [A Multi-purpose Realistic Haze Benchmark with Quantifiable Haze Levels and Ground Truth](https://mlsanity.com/paper/2206.06427/) (2022)
- [Photon-Starved Scene Inference using Single Photon Cameras](https://mlsanity.com/paper/2107.11001/) (2021)
- [Exploiting Diffusion Prior for Task-driven Image Restoration](https://mlsanity.com/paper/2507.22459/) (2025)
- [Lesion-Inspired Denoising Network: Connecting Medical Image Denoising and Lesion Detection](https://mlsanity.com/paper/2104.08845/) (2021)
- [A Comparison of Image Denoising Methods](https://mlsanity.com/paper/2304.08990/) (2023)
- [Backdoor Attacks against Image-to-Image Networks](https://mlsanity.com/paper/2407.10445/) (2024)
- [One Network to Solve Them All: A Sequential Multi-Task Joint Learning Network Framework for MR Imaging Pipeline](https://mlsanity.com/paper/2105.06653/) (2021)
- [DarkVision: A Benchmark for Low-light Image/Video Perception](https://mlsanity.com/paper/2301.06269/) (2023)
- [Prompt-SID: Learning Structural Representation Prompt via Latent Diffusion for Single-Image Denoising](https://mlsanity.com/paper/2502.06432/) (2025)
- [R3L: Connecting Deep Reinforcement Learning to Recurrent Neural Networks for Image Denoising via Residual Recovery](https://mlsanity.com/paper/2107.05318/) (2021)
- [Synergy Between Semantic Segmentation and Image Denoising via Alternate Boosting](https://mlsanity.com/paper/2102.12095/) (2021)
- [Priors in Deep Image Restoration and Enhancement: A Survey](https://mlsanity.com/paper/2206.02070/) (2022)
- [TaskTok: Delving into Task Tokens for Task-driven Image Restoration](https://mlsanity.com/paper/2606.26615/) (2026)
- [A Global Appearance and Local Coding Distortion based Fusion Framework for CNN based Filtering in Video Coding](https://mlsanity.com/paper/2106.12746/) (2021)
- [CERL: A Unified Optimization Framework for Light Enhancement with Realistic Noise](https://mlsanity.com/paper/2108.00478/) (2021)
- [Interactive Image Inpainting Using Semantic Guidance](https://mlsanity.com/paper/2201.10753/) (2022)
- [Deep Semantic Statistics Matching (D2SM) Denoising Network](https://mlsanity.com/paper/2207.09302/) (2022)
- [Robust Scene Inference under Noise-Blur Dual Corruptions](https://mlsanity.com/paper/2207.11643/) (2022)
- [DnSwin: Toward Real-World Denoising via Continuous Wavelet Sliding-Transformer](https://mlsanity.com/paper/2207.13861/) (2022)
- [FriendNet: Detection-Friendly Dehazing Network](https://mlsanity.com/paper/2403.04443/) (2024)
- [D-YOLO a robust framework for object detection in adverse weather conditions](https://mlsanity.com/paper/2403.09233/) (2024)
- [A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation](https://mlsanity.com/paper/2506.19406/) (2025)
- [A Note on Machine Learning Approach for Computational Imaging](https://mlsanity.com/paper/2202.11883/) (2022)
- [Generalizable Denoising of Microscopy Images using Generative Adversarial Networks and Contrastive Learning](https://mlsanity.com/paper/2303.15214/) (2023)

## Abstract (arXiv)

Image denoising and high-level vision tasks are usually handled independently in the conventional practice of computer vision, and their connection is fragile. In this paper, we cope with the two jointly and explore the mutual influence between them with the focus on two questions, namely (1) how image denoising can help improving high-level vision tasks, and (2) how the semantic information from high-level vision tasks can be used to guide image denoising. First for image denoising we propose a convolutional neural network in which convolutions are conducted in various spatial resolutions via downsampling and upsampling operations in order to fuse and exploit contextual information on different scales. Second we propose a deep neural network solution that cascades two modules for image denoising and various high-level tasks, respectively, and use the joint loss for updating only the denoising network via back-propagation. We experimentally show that on one hand, the proposed denoiser has the generality to overcome the performance degradation of different high-level vision tasks. On the other hand, with the guidance of high-level vision information, the denoising network produces more visually appealing results. Extensive experiments demonstrate the benefit of exploiting image semantics simultaneously for image denoising and high-level vision tasks via deep learning. The code is available online: https://github.com/Ding-Liu/DeepDenoising