258 research outputs found
Learned Perceptual Image Enhancement
Learning a typical image enhancement pipeline involves minimization of a loss
function between enhanced and reference images. While L1 and L2 losses are
perhaps the most widely used functions for this purpose, they do not
necessarily lead to perceptually compelling results. In this paper, we show
that adding a learned no-reference image quality metric to the loss can
significantly improve enhancement operators. This metric is implemented using a
CNN (convolutional neural network) trained on a large-scale dataset labelled
with aesthetic preferences of human raters. This loss allows us to conveniently
perform back-propagation in our learning framework to simultaneously optimize
for similarity to a given ground truth reference and perceptual quality. This
perceptual loss is only used to train parameters of image processing operators,
and does not impose any extra complexity at inference time. Our experiments
demonstrate that this loss can be effective for tuning a variety of operators
such as local tone mapping and dehazing
Personalised aesthetics with residual adapters
The use of computational methods to evaluate aesthetics in photography has
gained interest in recent years due to the popularization of convolutional
neural networks and the availability of new annotated datasets. Most studies in
this area have focused on designing models that do not take into account
individual preferences for the prediction of the aesthetic value of pictures.
We propose a model based on residual learning that is capable of learning
subjective, user specific preferences over aesthetics in photography, while
surpassing the state-of-the-art methods and keeping a limited number of
user-specific parameters in the model. Our model can also be used for picture
enhancement, and it is suitable for content-based or hybrid recommender systems
in which the amount of computational resources is limited.Comment: 12 pages, 4 figures. In Iberian Conference on Pattern Recognition and
Image Analysis proceeding
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