Haze Visibility Enhancement: A Survey and Quantitative Benchmarking
July 21, 2016 ยท The Cartographer ยท ๐ Computer Vision and Image Understanding
"No code URL or promise found in abstract"
"Title-pattern auto-detect: Haze Visibility Enhancement: A Survey and Quantitative Benchmarking"
Evidence collected by the PWNC Scanner
Authors
Yu Li, Shaodi You, Michael S. Brown, Robby T. Tan
arXiv ID
1607.06235
Category
cs.CV: Computer Vision
Citations
161
Venue
Computer Vision and Image Understanding
Last Checked
1 day ago
Abstract
This paper provides a comprehensive survey of methods dealing with visibility enhancement of images taken in hazy or foggy scenes. The survey begins with discussing the optical models of atmospheric scattering media and image formation. This is followed by a survey of existing methods, which are grouped to multiple image methods, polarizing filters based methods, methods with known depth, and single-image methods. We also provide a benchmark of a number of well known single-image methods, based on a recent dataset provided by Fattal and our newly generated scattering media dataset that contains ground truth images for quantitative evaluation. To our knowledge, this is the first benchmark using numerical metrics to evaluate dehazing techniques. This benchmark allows us to objectively compare the results of existing methods and to better identify the strengths and limitations of each method.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
๐ Similar Papers
In the same crypt โ Computer Vision
๐
๐
Old Age
๐
๐
Old Age
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
๐
๐
Old Age
SSD: Single Shot MultiBox Detector
๐
๐
Old Age
Squeeze-and-Excitation Networks
๐
๐
Old Age
Fast R-CNN
๐
๐
Old Age