193 research outputs found
Physical-based optimization for non-physical image dehazing methods
Images captured under hazy conditions (e.g. fog, air pollution) usually present faded colors and loss of contrast. To improve their visibility, a process called image dehazing can be applied. Some of the most successful image dehazing algorithms are based on image processing methods but do not follow any physical image formation model, which limits their performance. In this paper, we propose a post-processing technique to alleviate this handicap by enforcing the original method to be consistent with a popular physical model for image formation under haze. Our results improve upon those of the original methods qualitatively and according to several metrics, and they have also been validated via psychophysical experiments. These results are particularly striking in terms of avoiding over-saturation and reducing color artifacts, which are the most common shortcomings faced by image dehazing methods
Proximal Alternating Direction Network: A Globally Converged Deep Unrolling Framework
Deep learning models have gained great success in many real-world
applications. However, most existing networks are typically designed in
heuristic manners, thus lack of rigorous mathematical principles and
derivations. Several recent studies build deep structures by unrolling a
particular optimization model that involves task information. Unfortunately,
due to the dynamic nature of network parameters, their resultant deep
propagation networks do \emph{not} possess the nice convergence property as the
original optimization scheme does. This paper provides a novel proximal
unrolling framework to establish deep models by integrating experimentally
verified network architectures and rich cues of the tasks. More importantly, we
\emph{prove in theory} that 1) the propagation generated by our unrolled deep
model globally converges to a critical-point of a given variational energy, and
2) the proposed framework is still able to learn priors from training data to
generate a convergent propagation even when task information is only partially
available. Indeed, these theoretical results are the best we can ask for,
unless stronger assumptions are enforced. Extensive experiments on various
real-world applications verify the theoretical convergence and demonstrate the
effectiveness of designed deep models
Visibility recovery on images acquired in attenuating media. Application to underwater, fog, and mammographic imaging
136 p.When acquired in attenuating media, digital images of ten suffer from a particularly complex degradation that reduces their visual quality, hindering their suitability for further computational applications, or simply decreasing the visual pleasan tness for the user. In these cases, mathematical image processing reveals it self as an ideal tool to recover some of the information lost during the degradation process. In this dissertation,we deal with three of such practical scenarios in which this problematic is specially relevant, namely, underwater image enhancement, fogremoval and mammographic image processing. In the case of digital mammograms,X-ray beams traverse human tissue, and electronic detectorscapture them as they reach the other side. However, the superposition on a bidimensional image of three-dimensional structures produces low contraste dimages in which structures of interest suffer from a diminished visibility, obstructing diagnosis tasks. 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 all 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 theoreticaland computational tools, including image-dependent functional energies that capture the particularities of each degradation model. These energie sare composed of different integral terms that are simultaneous lyminimized by means of efficient numerical schemes, producing a clean,visually-pleasant and use ful output image, with better contrast and increased visibility. In every considered application, we provide comprehensive qualitative (visual) and quantitative experimental results to validateour methods, confirming that the developed techniques out perform other existing approaches in the literature
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