AOSR-Net: All-in-One Sandstorm Removal Network

TL;DR

AOSR-Net is a novel all-in-one deep learning model that directly restores sandstorm-affected images by integrating a re-formulated scattering model, outperforming existing methods in real-world sandstorm image enhancement.

Contribution

The paper introduces a unified sandstorm removal network based on a re-formulated scattering model, simplifying the process and improving generalization and effectiveness.

Findings

Outperforms state-of-the-art algorithms on synthetic and real images

Effectively addresses over-enhancement issues

Demonstrates superior generalization in real-world scenarios

Abstract

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.