245 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
Morphing active contours: a geometric approach to topology-independent image segmentation, tracking, interpolation and morphing
Region tracking on surfaces deforming via level-sets methods
PostprintSince the work by Osher and Sethian (1988) on level-sets algorithms for numerical shape evolutions, this technique has been used for a large number of applications in numerous fields. In medical imaging, this numerical technique has been successfully used, for example, in segmentation and cortex unfolding algorithms. The migration from a Lagrangian implementation to a Eulerian one via implicit representations or level-sets brought some of the main advantages of the technique, i.e., topology independence and stability. This migration means also that the evolution is parametrization free. Therefore, the authors do not know exactly how each part of the shape is deforming and the point-wise correspondence is lost. In this note they present a technique to numerically track regions on surfaces that are being deformed using the level-sets method. The basic idea is to represent the region of interest as the intersection of two implicit surfaces and then track its deformation from the deformation of these surfaces. This technique then solves one of the main shortcomings of the very useful level-sets approach. Applications include lesion localization in medical images, region tracking in functional MRI (fMRI) visualization, and geometric surface mapping
Divisive Normalization from Wilson-Cowan Dynamics
Divisive Normalization and the Wilson-Cowan equations are influential models
of neural interaction and saturation [Carandini and Heeger Nat.Rev.Neurosci.
2012; Wilson and Cowan Kybernetik 1973]. However, they have not been
analytically related yet. In this work we show that Divisive Normalization can
be obtained from the Wilson-Cowan model. Specifically, assuming that Divisive
Normalization is the steady state solution of the Wilson-Cowan differential
equation, we find that the kernel that controls neural interactions in Divisive
Normalization depends on the Wilson-Cowan kernel but also has a
signal-dependent contribution. A standard stability analysis of a Wilson-Cowan
model with the parameters obtained from our relation shows that the Divisive
Normalization solution is a stable node. This stability demonstrates the
consistency of our steady state assumption, and is in line with the
straightforward use of Divisive Normalization with time-varying stimuli.
The proposed theory provides a physiological foundation (a relation to a
dynamical network with fixed wiring among neurons) for the functional
suggestions that have been done on the need of signal-dependent Divisive
Normalization [e.g. in Coen-Cagli et al., PLoS Comp.Biol. 2012]. Moreover, this
theory explains the modifications that had to be introduced ad-hoc in Gaussian
kernels of Divisive Normalization in [Martinez et al. Front. Neurosci. 2019] to
reproduce contrast responses. The proposed relation implies that the
Wilson-Cowan dynamics also reproduces visual masking and subjective image
distortion metrics, which up to now had been mainly explained via Divisive
Normalization. Finally, this relation allows to apply to Divisive Normalization
the methods which up to now had been developed for dynamical systems such as
Wilson-Cowan networks
Morphing active contours : a geometric approach to topology-independent image segmentation and tracking
PostprintA method for deforming curves in a given image to a desired position in a second image is introduced in this paper. The algorithm is based on deforming the first image toward the second one via a partial differential equation, while tracking the deformation of the curves of interest in the first image with an additional, coupled, partial differential equation. The technique can be applied to object tracking and slice-by-slice segmentation of 3D data. The topology of the deforming curve can change, without any special topology handling procedures added to the scheme. This permits for example the automatic tracking of scenes where, due to occlusions, the topology of the objects of interest changes from frame to frame
Using Decoupled Features for Photo-realistic Style Transfer
In this work we propose a photorealistic style transfer method for image and
video that is based on vision science principles and on a recent mathematical
formulation for the deterministic decoupling of sample statistics. The novel
aspects of our approach include matching decoupled moments of higher order than
in common style transfer approaches, and matching a descriptor of the power
spectrum so as to characterize and transfer diffusion effects between source
and target, which is something that has not been considered before in the
literature. The results are of high visual quality, without spatio-temporal
artifacts, and validation tests in the form of observer preference experiments
show that our method compares very well with the state-of-the-art. The
computational complexity of the algorithm is low, and we propose a numerical
implementation that is amenable for real-time video application. Finally,
another contribution of our work is to point out that current deep learning
approaches for photorealistic style transfer don't really achieve
photorealistic quality outside of limited examples, because the results too
often show unacceptable visual artifacts
Convolutional Neural Networks Can Be Deceived by Visual Illusions
Visual illusions teach us that what we see is not always what is represented in the physical world. Their special nature make them a fascinating tool to test and validate any new vision model proposed. In general, current vision models are based on the concatenation of linear and non-linear operations. The similarity of this structure with the operations present in Convolutional Neural Networks (CNNs) has motivated us to study if CNNs trained for low-level visual tasks are deceived by visual illusions. In particular, we show that CNNs trained for image denoising, image deblurring, and computational color constancy are able to replicate the human response to visual illusions, and that the extent of this replication varies with respect to variation in architecture and spatial pattern size. These results suggest that in order to obtain CNNs that better replicate human behaviour, we may need to start aiming for them to better replicate visual illusions
Image inpainting
Trabajo presentado en la 27th Annual Conference on Computer Graphics and Interactive TechniquesInpainting, the technique of modifying an image in an undetectable form, is as ancient as art itself. The goals and applications of inpainting are numerous, from the restoration of damaged paintings and photographs to the removal/replacement of selected objects. In this paper, we introduce a novel algorithm for digital inpainting of still images that attempts to replicate the basic techniques used by professional restorators. After the user selects the regions to be restored, the algorithm automatically fills-in these regions with information surrounding them. The fill-in is done in such a way that isophote lines arriving at the regions’ boundaries are completed inside. In contrast with previous approaches, the technique here introduced does not require the user to specify where the novel information comes from. This is automatically done (and in a fast way), thereby allowing to simultaneously fill-in numerous regions containing completely different structures and surrounding backgrounds. In addition, no limitations are imposed on the topology of the region to be inpainted. Applications of this technique include the restoration of old photographs and damaged film; removal of superimposed text like dates, subtitles, or publicity; and the removal of entire objects from the image like microphones or wires in special effects
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