Papers › Fast Single Image Reflection Suppression via Convex Optimization
Fast Single Image Reflection Suppression via Convex Optimization
Yang Yang, Wenye Ma, Yin Zheng, Jian-Feng Cai, Weiyu Xu
Removing undesired reflections from images taken through the glass is of great importance in computer vision. It serves as a means to enhance the image quality for aesthetic purposes as well as to preprocess images in machine learning and pattern recognition applications. We propose a convex model to suppress the reflection from a single input image. Our model implies a partial differential equation with gradient thresholding, which is solved efficiently using Discrete Cosine Transform. Extensive experiments on synthetic and real-world images demonstrate that our approach achieves desirable reflection suppression results and dramatically reduces the execution time.
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