11,977 research outputs found
Piecewise Linear Model-Based Image Enhancement
A novel technique for the sharpening of noisy images is presented. The proposed enhancement system adopts a simple piecewise linear (PWL) function in order to sharpen the image edges and to reduce the noise. Such effects can easily be controlled by varying two parameters only. The noise sensitivity of the operator is further decreased by means of an additional filtering step, which resorts to a nonlinear model too. Results of computer simulations show that the proposed sharpening system is simple and effective. The application of the method to contrast enhancement of color images is also discussed
FRESH – FRI-based single-image super-resolution algorithm
In this paper, we consider the problem of single image super-resolution and propose a novel algorithm that outperforms state-of-the-art methods without the need of learning patches pairs from external data sets. We achieve this by modeling images and, more precisely, lines of images as piecewise smooth functions and propose a resolution enhancement method for this type of functions. The method makes use of the theory of sampling signals with finite rate of innovation (FRI) and combines it with traditional linear reconstruction methods. We combine the two reconstructions by leveraging from the multi-resolution analysis in wavelet theory and show how an FRI reconstruction and a linear reconstruction can be fused using filter banks. We then apply this method along vertical, horizontal, and diagonal directions in an image to obtain a single-image super-resolution algorithm. We also propose a further improvement of the method based on learning from the errors of our super-resolution result at lower resolution levels. Simulation results show that our method outperforms state-of-the-art algorithms under different blurring kernels
Aesthetic-Driven Image Enhancement by Adversarial Learning
We introduce EnhanceGAN, an adversarial learning based model that performs
automatic image enhancement. Traditional image enhancement frameworks typically
involve training models in a fully-supervised manner, which require expensive
annotations in the form of aligned image pairs. In contrast to these
approaches, our proposed EnhanceGAN only requires weak supervision (binary
labels on image aesthetic quality) and is able to learn enhancement operators
for the task of aesthetic-based image enhancement. In particular, we show the
effectiveness of a piecewise color enhancement module trained with weak
supervision, and extend the proposed EnhanceGAN framework to learning a deep
filtering-based aesthetic enhancer. The full differentiability of our image
enhancement operators enables the training of EnhanceGAN in an end-to-end
manner. We further demonstrate the capability of EnhanceGAN in learning
aesthetic-based image cropping without any groundtruth cropping pairs. Our
weakly-supervised EnhanceGAN reports competitive quantitative results on
aesthetic-based color enhancement as well as automatic image cropping, and a
user study confirms that our image enhancement results are on par with or even
preferred over professional enhancement
Segmentation and Restoration of Images on Surfaces by Parametric Active Contours with Topology Changes
In this article, a new method for segmentation and restoration of images on
two-dimensional surfaces is given. Active contour models for image segmentation
are extended to images on surfaces. The evolving curves on the surfaces are
mathematically described using a parametric approach. For image restoration, a
diffusion equation with Neumann boundary conditions is solved in a
postprocessing step in the individual regions. Numerical schemes are presented
which allow to efficiently compute segmentations and denoised versions of
images on surfaces. Also topology changes of the evolving curves are detected
and performed using a fast sub-routine. Finally, several experiments are
presented where the developed methods are applied on different artificial and
real images defined on different surfaces
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