13,984 research outputs found
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
Rectification from Radially-Distorted Scales
This paper introduces the first minimal solvers that jointly estimate lens
distortion and affine rectification from repetitions of rigidly transformed
coplanar local features. The proposed solvers incorporate lens distortion into
the camera model and extend accurate rectification to wide-angle images that
contain nearly any type of coplanar repeated content. We demonstrate a
principled approach to generating stable minimal solvers by the Grobner basis
method, which is accomplished by sampling feasible monomial bases to maximize
numerical stability. Synthetic and real-image experiments confirm that the
solvers give accurate rectifications from noisy measurements when used in a
RANSAC-based estimator. The proposed solvers demonstrate superior robustness to
noise compared to the state-of-the-art. The solvers work on scenes without
straight lines and, in general, relax the strong assumptions on scene content
made by the state-of-the-art. Accurate rectifications on imagery that was taken
with narrow focal length to near fish-eye lenses demonstrate the wide
applicability of the proposed method. The method is fully automated, and the
code is publicly available at https://github.com/prittjam/repeats.Comment: pre-prin
Imaging with a small number of photons
Low-light-level imaging techniques have application in many diverse fields,
ranging from biological sciences to security. We demonstrate a single-photon
imaging system based on a time-gated inten- sified CCD (ICCD) camera in which
the image of an object can be inferred from very few detected photons. We show
that a ghost-imaging configuration, where the image is obtained from photons
that have never interacted with the object, is a useful approach for obtaining
images with high signal-to-noise ratios. The use of heralded single-photons
ensures that the background counts can be virtually eliminated from the
recorded images. By applying techniques of compressed sensing and associated
image reconstruction, we obtain high-quality images of the object from raw data
comprised of fewer than one detected photon per image pixel.Comment: 9 pages, 4 figure
SADA: Semantic Adversarial Diagnostic Attacks for Autonomous Applications
One major factor impeding more widespread adoption of deep neural networks
(DNNs) is their lack of robustness, which is essential for safety-critical
applications such as autonomous driving. This has motivated much recent work on
adversarial attacks for DNNs, which mostly focus on pixel-level perturbations
void of semantic meaning. In contrast, we present a general framework for
adversarial attacks on trained agents, which covers semantic perturbations to
the environment of the agent performing the task as well as pixel-level
attacks. To do this, we re-frame the adversarial attack problem as learning a
distribution of parameters that always fools the agent. In the semantic case,
our proposed adversary (denoted as BBGAN) is trained to sample parameters that
describe the environment with which the black-box agent interacts, such that
the agent performs its dedicated task poorly in this environment. We apply
BBGAN on three different tasks, primarily targeting aspects of autonomous
navigation: object detection, self-driving, and autonomous UAV racing. On these
tasks, BBGAN can generate failure cases that consistently fool a trained agent.Comment: Accepted at AAAI'2
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