Aggregating Dual Contextual Features for Vehicle Glass Image Reflection Removal

In the field of intelligent transportation, the reflection phenomenon of vehicle glass may lead to blurring, distortion, or blocking of vehicle interior images captured by the surveillance system, thus forming a mixed image with reflections, which directly affects the surveillance system's accurate judgment and recognition of vehicle interior information. In view of the fact that most of the existing image reflection removal methods do not consider feature aggregation, which can easily cause color distortion and obvious artifacts, we propose a method to remove reflections from vehicle glass images by aggregating dual contextual features. Firstly, a multi-scale feature fusion of feature pyramid network is created to extract the spatial context features of the hybrid image, and obtain the required spatial information to improve the phenomenon of obvious artifacts; then an encoder-decoder structure is created that introduce the feature attention mechanism, and the channel context features of the hybrid image are extracted, and the required channel information is obtained to improve the color distortion phenomenon; finally, a feature-level aggregation network is constructed, which relies on the pixel attention mechanism to guide the feature aggregation, and realizes the removal of reflections from the vehicle glass image while effectively improving the recognizability of the background image. The experimental results on Real20 and SIR² test datasets show that the peak signal-to-noise ratio, structural similarity and minimum mean square error of the proposed method outperform those of existing reflection removal methods, and the problems of artifacts and color distortions have been significantly improved, which proves the effectiveness of the proposed method in the removal of image reflections.