Super-Resolution via Conditional Implicit Maximum Likelihood Estimation
| Title: | Super-Resolution via Conditional Implicit Maximum Likelihood Estimation |
|---|---|
| Authors: | Li, Ke, Peng, Shichong, Malik, Jitendra |
| Publication Year: | 2018 |
| Collection: | Computer Science Statistics |
| Subject Terms: | Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Graphics, Computer Science - Neural and Evolutionary Computing, Statistics - Machine Learning |
| More Details: | Single-image super-resolution (SISR) is a canonical problem with diverse applications. Leading methods like SRGAN produce images that contain various artifacts, such as high-frequency noise, hallucinated colours and shape distortions, which adversely affect the realism of the result. In this paper, we propose an alternative approach based on an extension of the method of Implicit Maximum Likelihood Estimation (IMLE). We demonstrate greater effectiveness at noise reduction and preservation of the original colours and shapes, yielding more realistic super-resolved images. Comment: 12 pages, 7 figures |
| Document Type: | Working Paper |
| Access URL: | http://arxiv.org/abs/1810.01406 |
| Accession Number: | edsarx.1810.01406 |
| Database: | arXiv |
| Description not available. |