Abstract
Removing reflections from an image is a very important task in image processing. Many times, we capture photographs of objects inside a glass. In such a case, the image has glass reflections. Sometimes these reflections are intense. There are many state-of-the-art methods for reflection removal. Although they produce good results, they cannot remove intense reflections. This paper focuses on removing intense reflections from an image. Most of the state-of-the-art methods focus on glass reflections. Specular highlights, another important component, commonly occur as a part of reflections. Specular highlights are strong and shiny dots. Reflections and specular highlights are image degradation artifacts that adversely affect visual quality and reduce the performance of computer vision applications. A Specular Highlights and Reflection Removal Network algorithm has been proposed. The algorithm removes both glass reflections and specular highlights. The Single Image Reflection Removal Dataset, a benchmark dataset, is used to conduct a range of experiments. The proposed model also shows a significant improvement over state-of-the-art technique. A lightweight deep learning model is used that works better when images have intense reflections. The results indicate that the proposed framework effectively suppresses reflections and specular highlights while preserving image details, making it suitable for applications in image enhancement, object recognition and computer vision systems.
Keywords: Deep Learning, Image Processing, Single Image Reflection Removal, Specular Highlights.