Fast Single Image Reflection Suppression via Convex Optimization

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 compared to the state of the art.

[1]  Cewu Lu,et al.  Image smoothing via L0 gradient minimization , 2011, ACM Trans. Graph..

[2]  William T. Freeman,et al.  A computational approach for obstruction-free photography , 2015, ACM Trans. Graph..

[3]  Aichi Chien,et al.  An L1-based variational model for Retinex theory and its application to medical images , 2011, CVPR 2011.

[4]  S. Osher,et al.  A TV Bregman iterative model of Retinex theory , 2012 .

[5]  Jae-Young Sim,et al.  Reflection Removal Using Low-Rank Matrix Completion , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[6]  Guanghui Liu,et al.  Automatic Reflection Removal using Gradient Intensity and Motion Cues , 2016, ACM Multimedia.

[7]  Sabine Süsstrunk,et al.  Single Image Reflection Suppression , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Ramesh Raskar,et al.  Removing photography artifacts using gradient projection and flash-exposure sampling , 2005, SIGGRAPH 2005.

[9]  Ramesh Raskar,et al.  Removing photography artifacts using gradient projection and flash-exposure sampling , 2005, ACM Trans. Graph..

[10]  Anat Levin,et al.  User Assisted Separation of Reflections from a Single Image Using a Sparsity Prior , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Michael S. Brown,et al.  Single Image Layer Separation Using Relative Smoothness , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[12]  Xiaochun Cao,et al.  Robust Separation of Reflection from Multiple Images , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[13]  Frédo Durand,et al.  Reflection removal using ghosting cues , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[14]  Changshui Zhang,et al.  Blindly separating mixtures of multiple layers with spatial shifts , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[15]  Ah-Hwee Tan,et al.  Sparsity based reflection removal using external patch search , 2017, 2017 IEEE International Conference on Multimedia and Expo (ICME).

[16]  Jiaolong Yang,et al.  A Generic Deep Architecture for Single Image Reflection Removal and Image Smoothing (Supplementary Material) , 2017 .

[17]  Yu-Wing Tai,et al.  A Physically-Based Approach to Reflection Separation: From Physical Modeling to Constrained Optimization , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Edward H. Adelson,et al.  Separating reflections and lighting using independent components analysis , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[19]  Michael S. Brown,et al.  Exploiting Reflection Change for Automatic Reflection Removal , 2013, 2013 IEEE International Conference on Computer Vision.

[20]  Yoav Y. Schechner,et al.  Polarization-based decorrelation of transparent layers: The inclination angle of an invisible surface , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[21]  Ah-Hwee Tan,et al.  Depth of field guided reflection removal , 2016, 2016 IEEE International Conference on Image Processing (ICIP).

[22]  William H. Press,et al.  Numerical Recipes 3rd Edition: The Art of Scientific Computing , 2007 .