72,823 research outputs found
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
Joint Regression and Ranking for Image Enhancement
Research on automated image enhancement has gained momentum in recent years,
partially due to the need for easy-to-use tools for enhancing pictures captured
by ubiquitous cameras on mobile devices. Many of the existing leading methods
employ machine-learning-based techniques, by which some enhancement parameters
for a given image are found by relating the image to the training images with
known enhancement parameters. While knowing the structure of the parameter
space can facilitate search for the optimal solution, none of the existing
methods has explicitly modeled and learned that structure. This paper presents
an end-to-end, novel joint regression and ranking approach to model the
interaction between desired enhancement parameters and images to be processed,
employing a Gaussian process (GP). GP allows searching for ideal parameters
using only the image features. The model naturally leads to a ranking technique
for comparing images in the induced feature space. Comparative evaluation using
the ground-truth based on the MIT-Adobe FiveK dataset plus subjective tests on
an additional data-set were used to demonstrate the effectiveness of the
proposed approach.Comment: WACV 201
Task adapted reconstruction for inverse problems
The paper considers the problem of performing a task defined on a model
parameter that is only observed indirectly through noisy data in an ill-posed
inverse problem. A key aspect is to formalize the steps of reconstruction and
task as appropriate estimators (non-randomized decision rules) in statistical
estimation problems. The implementation makes use of (deep) neural networks to
provide a differentiable parametrization of the family of estimators for both
steps. These networks are combined and jointly trained against suitable
supervised training data in order to minimize a joint differentiable loss
function, resulting in an end-to-end task adapted reconstruction method. The
suggested framework is generic, yet adaptable, with a plug-and-play structure
for adjusting both the inverse problem and the task at hand. More precisely,
the data model (forward operator and statistical model of the noise) associated
with the inverse problem is exchangeable, e.g., by using neural network
architecture given by a learned iterative method. Furthermore, any task that is
encodable as a trainable neural network can be used. The approach is
demonstrated on joint tomographic image reconstruction, classification and
joint tomographic image reconstruction segmentation
Use of data to inform expert evaluative opinion in the comparison of hand images—the importance of scars
Evaluation of a likelihood ratio is widely recognised as the most logical and appropriate means of assessing and expressing the weight of expert scientific evidence. This paper describes the application of such an approach to cases involving the comparison of images of hands that contain visible scars. Such evidence is frequently provided in cases of alleged child sexual abuse in which images of the perpetrator’s hand are compared with images of the suspect/accused’s hand. We illustrate how data provided from a database of hand images can be used to inform the probabilities that are an essential part of evaluating a likelihood ratio and, hence, how data have a bearing on the appraisal of the weight of evidence that can be attributed when scars are present within an image
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