Battlefield Image Dehazing Based on Global Compensation Attention Mechanism
In modern war,images and other carriers are widely used to obtain information. However,the images obtained in foggy environment not only affect scene rendering,but also mask important features. In order to improve the use value of fog-degraded images in modern warfare,a battlefield image dehazing method based on the global compensation attention mechanism is proposed. A global compensation module is constructed to ensure the integrity of output image,and channel down sampling is added to restore the clear image. The dense residual module is used to learn the nonlinear mapping between degraded image and clear image. In addition,an attention mechanism is added to improve the flexible processing capability of the network. The network can fully learn the feature information by increasing the number of channels of the input image. The experimental results show that the proposed method achieves excellent results in both subjective and objective evaluation compared with classical or novel image dehazing methods. The proposed method fully combines the attention mechanism with the global compensation module to effectively alleviate the problem of battlefield image degradation. At the same time,it pays attention to feature enhancement to enable the complete presentation of information and ultimately achieve better performance.
万方数据
维普
