Image Exposure Correction Based on Dual Feature Fusion for Intelligent Transportation

In the field of intelligent transportation, the quality of vehicle images captured by surveillance cameras can be affected by the lighting conditions of the scene, resulting in the loss of critical information, which in turn leads to a lack of reliable image evidence for subsequent traffic enforcement. However, existing image exposure correction methods have problems such as color distortion and blurry details in the corrected image. Therefore, a dual feature fusion image exposure correction method for intelligent transportation is proposed, which extracts relevant features through global subnet and exposure subnet, respectively. The global subnet introduces the feature attention mechanism to extract global features of the image, improving the overall brightness and color of the corrected image. The exposure subnet is based on the U-Net structure and introduces a multi-scale attention mechanism to extract exposure features at different scales in the image, improving the detailed texture of the corrected image. Finally, image exposure correction is achieved by fusing the dual features and mapping them to the image space. The experimental results on the MSEC dataset demonstrate that, compared to the existing exposure correction methods, the proposed method has a peak signal-to-noise ratio of 21.801 5 dB, a structural similarity index measure of 0.867 7, and a natural image quality evaluator of 11.711 8. The proposed method can effectively restore color information and detailed textures in images, proving its excellent exposure correction effect.