AOSR-Net: All-in-One Sandstorm Removal Network
This addresses the issue of over-enhancement and weak generalization in sand dust image enhancement for applications like computer vision, though it appears incremental as it builds on existing scattering models.
The paper tackled the problem of sandstorm image enhancement by introducing AOSR-Net, a model that integrates intermediate parameters to directly map images, resulting in superior performance over state-of-the-art methods on synthetic and real-world datasets.
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.