Single Image Reflection Removal Using DeepLabv3+

When photographing near transparent objects such as glass, things in the area often appear in the image (reflected light). This reflected light degrades the image information and affects computer vision task such as object detection and segmentation. Separating reflected light in an image is a challenging task in computer vision. In this study, we create synthetic images and increase the number of training data. We proposed a reflection rejection method that used DeepLabv3+ as a deep learning model and measured the accuracy of the proposed method using PSNR and SSIM, commonly used evaluation methods for reflection rejection. The accuracy of the proposed method is improved compared to the conventional method due to the feature of DeepLabv3+ to obtain information in a wide range of contexts efficiently.