Abstract:

When acquired in attenuating media, digital images often suffer from a particularly complex degradation that reduces their visual quality, hindering their suitability for further computational applications, or simply decreasing the visual pleasantness for the user. In these cases, mathematical image processing reveals itself as an ideal tool to recover some of the information lost during the degradation process. In this talk, we deal with two of such practical scenarios in which this problematic is specially relevant, namely, underwater image enhancement and fog removal (image dehazing). Regarding fog removal, the loss of contrast is produced by the atmospheric conditions, and white colour takes over the scene uniformly as distance increases, also reducing visibility. For underwater images, there is an added difficulty, since colour is not lost uniformly; instead, red colours decay the fastest, and green and blue colours typically dominate the acquired images. To address these challenges, in this dissertation we develop new methodologies that rely on: a) physical models of the observed degradation, and b) the calculus of variations. Equipped with this powerful machinery, we design novel theoretical and computational tools, including image-dependent functional energies that capture the particularities of each degradation model. These energies are composed of different integral terms that are simultaneously minimized by means of efficient numerical schemes, producing a clean, visually-pleasant and useful output image, with better contrast and increased visibility. In both of the considered applications, we provide comprehensive qualitative (visual) and quantitative experimental results to validate our methods, confirming that the developed techniques outperform other existing approaches in the literature.

Bibtex:

@inproceedings{adrian_galdran_visibility_2016,
	address = {Instituto de Matemáticas, Pontificia Universidad Católica de Valparaíso, Chile},
	title = {Visibility {Recovery} on {Images} {Acquired} in {Attenuating} {Media}. {Application} to {Underwater} and {Fog} {Image} {Restoration}},
	language = {en},
	author = { {Adrián Galdran} and {Artzai Picón} and {Aitor Alvarez-Gila} and {Javier Vazquez-Corral} and {Marcelo Bertalmío} and {David Pardo} },
	month = jan,
	year = {2016}
	}