11,838 research outputs found
Example-based image color and tone style enhancement
Color and tone adjustments are among the most frequent image enhancement operations. We define a color and tone style as a set of explicit or implicit rules governing color and tone adjustments. Our goal in this paper is to learn implicit color and tone adjustment rules from examples. That is, given a set of examples, each of which is a pair of corresponding images before and after adjustments, we would like to discover the underlying mathematical relationships optimally connecting the color and tone of corresponding pixels in all image pairs. We formally define tone and color adjustment rules as mappings, and propose to approximate complicated spatially varying nonlinear mappings in a piecewise manner. The reason behind this is that a very complicated mapping can still be locally approximated with a low-order polynomial model. Parameters within such low-order models are trained using data extracted from example image pairs. We successfully apply our framework in two scenarios, low-quality photo enhancement by transferring the style of a high-end camera, and photo enhancement using styles learned from photographers and designers. © 2011 ACM.postprin
Automatic Image Stylization Using Deep Fully Convolutional Networks
Color and tone stylization strives to enhance unique themes with artistic
color and tone adjustments. It has a broad range of applications from
professional image postprocessing to photo sharing over social networks.
Mainstream photo enhancement softwares provide users with predefined styles,
which are often hand-crafted through a trial-and-error process. Such photo
adjustment tools lack a semantic understanding of image contents and the
resulting global color transform limits the range of artistic styles it can
represent. On the other hand, stylistic enhancement needs to apply distinct
adjustments to various semantic regions. Such an ability enables a broader
range of visual styles. In this paper, we propose a novel deep learning
architecture for automatic image stylization, which learns local enhancement
styles from image pairs. Our deep learning architecture is an end-to-end deep
fully convolutional network performing semantics-aware feature extraction as
well as automatic image adjustment prediction. Image stylization can be
efficiently accomplished with a single forward pass through our deep network.
Experiments on existing datasets for image stylization demonstrate the
effectiveness of our deep learning architecture
Exposure: A White-Box Photo Post-Processing Framework
Retouching can significantly elevate the visual appeal of photos, but many
casual photographers lack the expertise to do this well. To address this
problem, previous works have proposed automatic retouching systems based on
supervised learning from paired training images acquired before and after
manual editing. As it is difficult for users to acquire paired images that
reflect their retouching preferences, we present in this paper a deep learning
approach that is instead trained on unpaired data, namely a set of photographs
that exhibits a retouching style the user likes, which is much easier to
collect. Our system is formulated using deep convolutional neural networks that
learn to apply different retouching operations on an input image. Network
training with respect to various types of edits is enabled by modeling these
retouching operations in a unified manner as resolution-independent
differentiable filters. To apply the filters in a proper sequence and with
suitable parameters, we employ a deep reinforcement learning approach that
learns to make decisions on what action to take next, given the current state
of the image. In contrast to many deep learning systems, ours provides users
with an understandable solution in the form of conventional retouching edits,
rather than just a "black-box" result. Through quantitative comparisons and
user studies, we show that this technique generates retouching results
consistent with the provided photo set.Comment: ACM Transaction on Graphics (Accepted with minor revisions
NIMA: Neural Image Assessment
Automatically learned quality assessment for images has recently become a hot
topic due to its usefulness in a wide variety of applications such as
evaluating image capture pipelines, storage techniques and sharing media.
Despite the subjective nature of this problem, most existing methods only
predict the mean opinion score provided by datasets such as AVA [1] and TID2013
[2]. Our approach differs from others in that we predict the distribution of
human opinion scores using a convolutional neural network. Our architecture
also has the advantage of being significantly simpler than other methods with
comparable performance. Our proposed approach relies on the success (and
retraining) of proven, state-of-the-art deep object recognition networks. Our
resulting network can be used to not only score images reliably and with high
correlation to human perception, but also to assist with adaptation and
optimization of photo editing/enhancement algorithms in a photographic
pipeline. All this is done without need for a "golden" reference image,
consequently allowing for single-image, semantic- and perceptually-aware,
no-reference quality assessment.Comment: IEEE Transactions on Image Processing 201
Fully-automatic inverse tone mapping algorithm based on dynamic mid-level tone mapping
High Dynamic Range (HDR) displays can show images with higher color contrast levels and peak luminosities than the common Low Dynamic Range (LDR) displays. However, most existing video content is recorded and/or graded in LDR format. To show LDR content on HDR displays, it needs to be up-scaled using a so-called inverse tone mapping algorithm. Several techniques for inverse tone mapping have been proposed in the last years, going from simple approaches based on global and local operators to more advanced algorithms such as neural networks. Some of the drawbacks of existing techniques for inverse tone mapping are the need for human intervention, the high computation time for more advanced algorithms, limited low peak brightness, and the lack of the preservation of the artistic intentions. In this paper, we propose a fully-automatic inverse tone mapping operator based on mid-level mapping capable of real-time video processing. Our proposed algorithm allows expanding LDR images into HDR images with peak brightness over 1000 nits, preserving the artistic intentions inherent to the HDR domain. We assessed our results using the full-reference objective quality metrics HDR-VDP-2.2 and DRIM, and carrying out a subjective pair-wise comparison experiment. We compared our results with those obtained with the most recent methods found in the literature. Experimental results demonstrate that our proposed method outperforms the current state-of-the-art of simple inverse tone mapping methods and its performance is similar to other more complex and time-consuming advanced techniques
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
Cross-Platform Presentation of Interactive Volumetric Imagery
Volume data is useful across many disciplines, not just medicine.
Thus, it is very important that researchers have a simple and
lightweight method of sharing and reproducing such volumetric
data. In this paper, we explore some of the challenges associated
with volume rendering, both from a classical sense and from the
context of Web3D technologies. We describe and evaluate the pro-
posed X3D Volume Rendering Component and its associated styles
for their suitability in the visualization of several types of image
data. Additionally, we examine the ability for a minimal X3D node
set to capture provenance and semantic information from outside
ontologies in metadata and integrate it with the scene graph
Learning Digital Camera Pipeline for Extreme Low-Light Imaging
In low-light conditions, a conventional camera imaging pipeline produces
sub-optimal images that are usually dark and noisy due to a low photon count
and low signal-to-noise ratio (SNR). We present a data-driven approach that
learns the desired properties of well-exposed images and reflects them in
images that are captured in extremely low ambient light environments, thereby
significantly improving the visual quality of these low-light images. We
propose a new loss function that exploits the characteristics of both
pixel-wise and perceptual metrics, enabling our deep neural network to learn
the camera processing pipeline to transform the short-exposure, low-light RAW
sensor data to well-exposed sRGB images. The results show that our method
outperforms the state-of-the-art according to psychophysical tests as well as
pixel-wise standard metrics and recent learning-based perceptual image quality
measures
Learning to Globally Edit Images with Textual Description
We show how we can globally edit images using textual instructions: given a
source image and a textual instruction for the edit, generate a new image
transformed under this instruction. To tackle this novel problem, we develop
three different trainable models based on RNN and Generative Adversarial
Network (GAN). The models (bucket, filter bank, and end-to-end) differ in how
much expert knowledge is encoded, with the most general version being purely
end-to-end. To train these systems, we use Amazon Mechanical Turk to collect
textual descriptions for around 2000 image pairs sampled from several datasets.
Experimental results evaluated on our dataset validate our approaches. In
addition, given that the filter bank model is a good compromise between
generality and performance, we investigate it further by replacing RNN with
Graph RNN, and show that Graph RNN improves performance. To the best of our
knowledge, this is the first computational photography work on global image
editing that is purely based on free-form textual instructions
Bridging the Gap Between Computational Photography and Visual Recognition
What is the current state-of-the-art for image restoration and enhancement
applied to degraded images acquired under less than ideal circumstances? Can
the application of such algorithms as a pre-processing step to improve image
interpretability for manual analysis or automatic visual recognition to
classify scene content? While there have been important advances in the area of
computational photography to restore or enhance the visual quality of an image,
the capabilities of such techniques have not always translated in a useful way
to visual recognition tasks. Consequently, there is a pressing need for the
development of algorithms that are designed for the joint problem of improving
visual appearance and recognition, which will be an enabling factor for the
deployment of visual recognition tools in many real-world scenarios. To address
this, we introduce the UG^2 dataset as a large-scale benchmark composed of
video imagery captured under challenging conditions, and two enhancement tasks
designed to test algorithmic impact on visual quality and automatic object
recognition. Furthermore, we propose a set of metrics to evaluate the joint
improvement of such tasks as well as individual algorithmic advances, including
a novel psychophysics-based evaluation regime for human assessment and a
realistic set of quantitative measures for object recognition performance. We
introduce six new algorithms for image restoration or enhancement, which were
created as part of the IARPA sponsored UG^2 Challenge workshop held at CVPR
2018. Under the proposed evaluation regime, we present an in-depth analysis of
these algorithms and a host of deep learning-based and classic baseline
approaches. From the observed results, it is evident that we are in the early
days of building a bridge between computational photography and visual
recognition, leaving many opportunities for innovation in this area.Comment: CVPR Prize Challenge: http://www.ug2challenge.or
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