Abstract:

Images of target scenes shot through clear, reflective materials like glass are frequently interfered by unwanted reflection scenes which often overlaid on top of the targeted scenes. This, has constantly degrades the quality of the captured images and affects their subsequent analyses. While cognitively, distinguishing a recognizable object from its reflection in a picture is not difficult for humans, it is highly difficult and more complex in computer vision due to the ill-posed nature of the problem. In this research an enhanced single-image reflection removal model was developed by combining Gaussian filter and deep learning encoder-decoder for effective performance. While the Gaussian filter denoises the reflection-contaminated image, the encoder-decoder network learns the features of the image to produce reflection-free image. The proposed network is an end-to-end trained network with three losses. The experimental findings showed that the proposed model out-performed several state-of-the-art methods both qualitatively and quantitatively on five different datasets.