21,175 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
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
We present a new method for synthesizing high-resolution photo-realistic
images from semantic label maps using conditional generative adversarial
networks (conditional GANs). Conditional GANs have enabled a variety of
applications, but the results are often limited to low-resolution and still far
from realistic. In this work, we generate 2048x1024 visually appealing results
with a novel adversarial loss, as well as new multi-scale generator and
discriminator architectures. Furthermore, we extend our framework to
interactive visual manipulation with two additional features. First, we
incorporate object instance segmentation information, which enables object
manipulations such as removing/adding objects and changing the object category.
Second, we propose a method to generate diverse results given the same input,
allowing users to edit the object appearance interactively. Human opinion
studies demonstrate that our method significantly outperforms existing methods,
advancing both the quality and the resolution of deep image synthesis and
editing.Comment: v2: CVPR camera ready, adding more results for edge-to-photo example
Cross-Section Bead Image Prediction in Laser Keyhole Welding of AISI 1020 Steel Using Deep Learning Architectures
A deep learning model was applied for predicting a cross-sectional bead image from laser welding process parameters. The proposed model consists of two successive generators. The first generator produces a weld bead segmentation map from laser intensity and interaction time, which is subsequently translated into an optical microscopic (OM) image by the second generator. Both generators exhibit an encoder & x2013;decoder structure based on a convolutional neural network (CNN). In the second generator, a conditional generative adversarial network (cGAN) was additionally employed with multiscale discriminators and residual blocks, considering the size of the OM image. For a training dataset, laser welding experiments with AISI 1020 steel were conducted on a large process window using a 2 KW fiber laser, and a total of 39 process conditions were used for the training. High-resolution OM images were successfully generated, and the predicted bead shapes were reasonably accurate (R-Squared: 89.0 & x0025; for penetration depth, 93.6 & x0025; for weld bead area)
WESPE: Weakly Supervised Photo Enhancer for Digital Cameras
Low-end and compact mobile cameras demonstrate limited photo quality mainly
due to space, hardware and budget constraints. In this work, we propose a deep
learning solution that translates photos taken by cameras with limited
capabilities into DSLR-quality photos automatically. We tackle this problem by
introducing a weakly supervised photo enhancer (WESPE) - a novel image-to-image
Generative Adversarial Network-based architecture. The proposed model is
trained by under weak supervision: unlike previous works, there is no need for
strong supervision in the form of a large annotated dataset of aligned
original/enhanced photo pairs. The sole requirement is two distinct datasets:
one from the source camera, and one composed of arbitrary high-quality images
that can be generally crawled from the Internet - the visual content they
exhibit may be unrelated. Hence, our solution is repeatable for any camera:
collecting the data and training can be achieved in a couple of hours. In this
work, we emphasize on extensive evaluation of obtained results. Besides
standard objective metrics and subjective user study, we train a virtual rater
in the form of a separate CNN that mimics human raters on Flickr data and use
this network to get reference scores for both original and enhanced photos. Our
experiments on the DPED, KITTI and Cityscapes datasets as well as pictures from
several generations of smartphones demonstrate that WESPE produces comparable
or improved qualitative results with state-of-the-art strongly supervised
methods
Multiple conserved regulatory domains promote Fezf2 expression in the developing cerebral cortex.
BackgroundThe genetic programs required for development of the cerebral cortex are under intense investigation. However, non-coding DNA elements that control the expression of developmentally important genes remain poorly defined. Here we investigate the regulation of Fezf2, a transcription factor that is necessary for the generation of deep-layer cortical projection neurons.ResultsUsing a combination of chromatin immunoprecipitation followed by high throughput sequencing (ChIP-seq) we mapped the binding of four deep-layer-enriched transcription factors previously shown to be important for cortical development. Building upon this we characterized the activity of three regulatory regions around the Fezf2 locus at multiple stages throughout corticogenesis. We identified a promoter that was sufficient for expression in the cerebral cortex, and enhancers that drove reporter gene expression in distinct forebrain domains, including progenitor cells and cortical projection neurons.ConclusionsThese results provide insight into the regulatory logic controlling Fezf2 expression and further the understanding of how multiple non-coding regulatory domains can collaborate to control gene expression in vivo
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Pluripotency factors functionally premark cell-type-restricted enhancers in ES cells.
Enhancers for embryonic stem (ES) cell-expressed genes and lineage-determining factors are characterized by conventional marks of enhancer activation in ES cells1-3, but it remains unclear whether enhancers destined to regulate cell-type-restricted transcription units might also have distinct signatures in ES cells. Here we show that cell-type-restricted enhancers are 'premarked' and activated as transcription units by the binding of one or two ES cell transcription factors, although they do not exhibit traditional enhancer epigenetic marks in ES cells, thus uncovering the initial temporal origins of cell-type-restricted enhancers. This premarking is required for future cell-type-restricted enhancer activity in the differentiated cells, with the strength of the ES cell signature being functionally important for the subsequent robustness of cell-type-restricted enhancer activation. We have experimentally validated this model in macrophage-restricted enhancers and neural precursor cell (NPC)-restricted enhancers using ES cell-derived macrophages or NPCs, edited to contain specific ES cell transcription factor motif deletions. DNA hydroxyl-methylation of enhancers in ES cells, determined by ES cell transcription factors, may serve as a potential molecular memory for subsequent enhancer activation in mature macrophages. These findings suggest that the massive repertoire of cell-type-restricted enhancers are essentially hierarchically and obligatorily premarked by binding of a defining ES cell transcription factor in ES cells, dictating the robustness of enhancer activation in mature cells
Residual magnifier: A dense information flow network for super resolution
© 2019 IEEE. Recently, deep learning methods have been successfully applied to single image super-resolution tasks. However, some networks with extreme depth failed to achieve better performance because of the insufficient utilization of the local residual information extracted at each stage. To solve the above question, we propose a Dense Information Flow Network (DIF-Net), which can fully extract and utilize the local residual information at each stage to accomplish a better reconstruction. Specifically, we present a Two-stage Residual Extraction Block (TREB) to extract the shallow and deep local residual information at each stage. The dense connection mechanism is introduced throughout the model and within TREBs to dramatically increase the information flow. Meanwhile this mechanism prevents the shallow features extracted earlier from being diluted. Finally, we propose a lightweight subnet (residual enhancer) to efficiently recycle the overflow residual information from the backbone net for detail enhancement of the residual image. Experimental results demonstrate that the proposed method performs favorably against the state-of-the-art methods with relatively-less parameters
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