7,291 research outputs found
Learn from Unpaired Data for Image Restoration: A Variational Bayes Approach
Collecting paired training data is difficult in practice, but the unpaired
samples broadly exist. Current approaches aim at generating synthesized
training data from the unpaired samples by exploring the relationship between
the corrupted and clean data. This work proposes LUD-VAE, a deep generative
method to learn the joint probability density function from data sampled from
marginal distributions. Our approach is based on a carefully designed
probabilistic graphical model in which the clean and corrupted data domains are
conditionally independent. Using variational inference, we maximize the
evidence lower bound (ELBO) to estimate the joint probability density function.
Furthermore, we show that the ELBO is computable without paired samples under
the inference invariant assumption. This property provides the mathematical
rationale of our approach in the unpaired setting. Finally, we apply our method
to real-world image denoising and super-resolution tasks and train the models
using the synthetic data generated by the LUD-VAE. Experimental results
validate the advantages of our method over other learnable approaches
Real-World Image Super Resolution via Unsupervised Bi-directional Cycle Domain Transfer Learning based Generative Adversarial Network
Deep Convolutional Neural Networks (DCNNs) have exhibited impressive
performance on image super-resolution tasks. However, these deep learning-based
super-resolution methods perform poorly in real-world super-resolution tasks,
where the paired high-resolution and low-resolution images are unavailable and
the low-resolution images are degraded by complicated and unknown kernels. To
break these limitations, we propose the Unsupervised Bi-directional Cycle
Domain Transfer Learning-based Generative Adversarial Network (UBCDTL-GAN),
which consists of an Unsupervised Bi-directional Cycle Domain Transfer Network
(UBCDTN) and the Semantic Encoder guided Super Resolution Network (SESRN).
First, the UBCDTN is able to produce an approximated real-like LR image through
transferring the LR image from an artificially degraded domain to the
real-world LR image domain. Second, the SESRN has the ability to super-resolve
the approximated real-like LR image to a photo-realistic HR image. Extensive
experiments on unpaired real-world image benchmark datasets demonstrate that
the proposed method achieves superior performance compared to state-of-the-art
methods.Comment: 12 pages, 5 figures,3 tables. This work is submitted to IEEE
Transactions on Systems, Man, and Cybernetics: Systems (2022). It's under
review by IEEE Transactions on Systems, Man, and Cybernetics: Systems for no
Toward Real-World Light Field Super-Resolution
Deep learning has opened up new possibilities for light field
super-resolution (SR), but existing methods trained on synthetic datasets with
simple degradations (e.g., bicubic downsampling) suffer from poor performance
when applied to complex real-world scenarios. To address this problem, we
introduce LytroZoom, the first real-world light field SR dataset capturing
paired low- and high-resolution light fields of diverse indoor and outdoor
scenes using a Lytro ILLUM camera. Additionally, we propose the Omni-Frequency
Projection Network (OFPNet), which decomposes the omni-frequency components and
iteratively enhances them through frequency projection operations to address
spatially variant degradation processes present in all frequency components.
Experiments demonstrate that models trained on LytroZoom outperform those
trained on synthetic datasets and are generalizable to diverse content and
devices. Quantitative and qualitative evaluations verify the superiority of
OFPNet. We believe this work will inspire future research in real-world light
field SR.Comment: CVPRW 202
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