AOSR-Net: All-in-One Sandstorm Removal Network
Not on the map. Why?
Abstract (arXiv)
Most existing sandstorm image enhancement methods are based on traditional theory and prior knowledge, which often restrict their applicability in real-world scenarios. In addition, these approaches often adopt a strategy of color correction followed by dust removal, which makes the algorithm structure too complex. To solve the issue, we introduce a novel image restoration model, named all-in-one sandstorm removal network (AOSR-Net). This model is developed based on a re-formulated sandstorm scattering model, which directly establishes the image mapping relationship by integrating intermediate parameters. Such integration scheme effectively addresses the problems of over-enhancement and weak generalization in the field of sand dust image enhancement. Experimental results on synthetic and real-world sandstorm images demonstrate the superiority of the proposed AOSR-Net over state-of-the-art (SOTA) algorithms.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 13 of its 44 references that are in the map. First posted on arXiv 16 Sep 2023.
Builds on
13 of 44 references.
References · 13
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
- 2017mixup: Beyond Empirical Risk Minimizationcited by 5,183
- 2017Benchmarking Single Image Dehazing and Beyondcited by 447
- 2019On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networkscited by 305
- 2021Contrastive Learning for Compact Single Image Dehazingcited by 219
- 2015Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fieldscited by 169
- 2023QI-TTS: Questioning Intonation Control for Emotional Speech Synthesiscited by 8
- 2021Rank-One Prior: Toward Real-Time Scene Recoverycited by 7
- 2022A comprehensive benchmark analysis for sand dust image reconstructioncited by 3
- 2022Pose Guided Human Image Synthesis with Partially Decoupled GANcited by 3
- 2023PMVC: Data Augmentation-Based Prosody Modeling for Expressive Voice Conversioncited by 3
1 more
Built on by
Not cited yet.