4,185 research outputs found
DRASIC: Distributed Recurrent Autoencoder for Scalable Image Compression
We propose a new architecture for distributed image compression from a group
of distributed data sources. The work is motivated by practical needs of
data-driven codec design, low power consumption, robustness, and data privacy.
The proposed architecture, which we refer to as Distributed Recurrent
Autoencoder for Scalable Image Compression (DRASIC), is able to train
distributed encoders and one joint decoder on correlated data sources. Its
compression capability is much better than the method of training codecs
separately. Meanwhile, the performance of our distributed system with 10
distributed sources is only within 2 dB peak signal-to-noise ratio (PSNR) of
the performance of a single codec trained with all data sources. We experiment
distributed sources with different correlations and show how our data-driven
methodology well matches the Slepian-Wolf Theorem in Distributed Source Coding
(DSC). To the best of our knowledge, this is the first data-driven DSC
framework for general distributed code design with deep learning
SGUIE-Net: Semantic Attention Guided Underwater Image Enhancement with Multi-Scale Perception
Due to the wavelength-dependent light attenuation, refraction and scattering,
underwater images usually suffer from color distortion and blurred details.
However, due to the limited number of paired underwater images with undistorted
images as reference, training deep enhancement models for diverse degradation
types is quite difficult. To boost the performance of data-driven approaches,
it is essential to establish more effective learning mechanisms that mine
richer supervised information from limited training sample resources. In this
paper, we propose a novel underwater image enhancement network, called
SGUIE-Net, in which we introduce semantic information as high-level guidance
across different images that share common semantic regions. Accordingly, we
propose semantic region-wise enhancement module to perceive the degradation of
different semantic regions from multiple scales and feed it back to the global
attention features extracted from its original scale. This strategy helps to
achieve robust and visually pleasant enhancements to different semantic
objects, which should thanks to the guidance of semantic information for
differentiated enhancement. More importantly, for those degradation types that
are not common in the training sample distribution, the guidance connects them
with the already well-learned types according to their semantic relevance.
Extensive experiments on the publicly available datasets and our proposed
dataset demonstrated the impressive performance of SGUIE-Net. The code and
proposed dataset are available at: https://trentqq.github.io/SGUIE-Net.htm
HybrUR: A Hybrid Physical-Neural Solution for Unsupervised Underwater Image Restoration
Robust vision restoration for an underwater image remains a challenging
problem. For the lack of aligned underwater-terrestrial image pairs, the
unsupervised method is more suited to this task. However, the pure data-driven
unsupervised method usually has difficulty in achieving realistic color
correction for lack of optical constraint. In this paper, we propose a data-
and physics-driven unsupervised architecture that learns underwater vision
restoration from unpaired underwater-terrestrial images. For sufficient domain
transformation and detail preservation, the underwater degeneration needs to be
explicitly constructed based on the optically unambiguous physics law. Thus, we
employ the Jaffe-McGlamery degradation theory to design the generation models,
and use neural networks to describe the process of underwater degradation.
Furthermore, to overcome the problem of invalid gradient when optimizing the
hybrid physical-neural model, we fully investigate the intrinsic correlation
between the scene depth and the degradation factors for the backscattering
estimation, to improve the restoration performance through physical
constraints. Our experimental results show that the proposed method is able to
perform high-quality restoration for unconstrained underwater images without
any supervision. On multiple benchmarks, we outperform several state-of-the-art
supervised and unsupervised approaches. We also demonstrate that our methods
yield encouraging results on real-world applications
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