Regularizing images under a guidance signal has been used in various tasks in computer vision and computational photography, particularly for noise reduction and joint upsampling. The aim is to transfer fine structures of guidance signals to input images, restoring noisy or altered structures. One of main drawbacks in such a data-dependent framework is that it does not handle differences in structure between guidance and input images. We address this problem by jointly leveraging structural information of guidance and input images. Image filtering is formulated as a nonconvex optimization problem, which is solved by the majorization-minimization algorithm. The proposed algorithm converges quickly while guaranteeing a local minimum. It effectively controls image structures at different scales and can handle a variety of types of data from different sensors. We demonstrate the flexibility and effectiveness of our model in several applications including depth super-resolution, scale-space filtering, texture removal, flash/non-flash denoising, and RGB/NIR denoising.
|Title of host publication||IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015|
|Publisher||IEEE Computer Society|
|Number of pages||9|
|Publication status||Published - 2015 Oct 14|
|Event||IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015 - Boston, United States|
Duration: 2015 Jun 7 → 2015 Jun 12
|Name||Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition|
|Other||IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015|
|Period||15/6/7 → 15/6/12|
Bibliographical notePublisher Copyright:
© 2015 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition