Deblurring Network for UV Images of Colorless Chemicals Floating on Sea Surface

Abstract

Automatic imaging monitoring technology has been developed as a mature solution to address oil spill accidents. However, when airborne equipment is used to monitor the area of leakage, there is a chance of relative motion between the equipment and the target, leading to blurred images. This can hinder subsequent monitoring tasks, such as the identification and segmentation of hazardous chemical targets. Therefore, it is crucial to deblur these images, as sharper images can substantially improve the precision and accuracy of these tasks. In light of this, this paper presents a novel multi-scale recurrent deblurring network, leveraging the Convolutional Block Attention Module (CBAM) attention mechanism, to restore clear images in an end-to-end manner. The CBAM attention mechanism has been incorporated, which learns features in both the spatial and channel domains, thereby enhancing the deblurring ability of the network by combining the attention mechanisms of multiple domains. To evaluate the effectiveness of the proposed method, we applied the deblurring network to our self-built dataset of blurred ultraviolet (UV) images of colorless chemicals floating on water surfaces. Compared to the SRN-Deblur deblurring network, the proposed approach yields improved PSNR and SSIM results. Moreover, when compared with the state-of-the-art DeblurGANv2 method, it performs better in the SSIM. These results demonstrate that the proposed multi-scale recurrent deblurring network, based on the CBAM attention mechanism, exhibits a superior deblurring performance.