102 research outputs found
Designing A Composite Dictionary Adaptively From Joint Examples
We study the complementary behaviors of external and internal examples in
image restoration, and are motivated to formulate a composite dictionary design
framework. The composite dictionary consists of the global part learned from
external examples, and the sample-specific part learned from internal examples.
The dictionary atoms in both parts are further adaptively weighted to emphasize
their model statistics. Experiments demonstrate that the joint utilization of
external and internal examples leads to substantial improvements, with
successful applications in image denoising and super resolution
"Zero-Shot" Super-Resolution using Deep Internal Learning
Deep Learning has led to a dramatic leap in Super-Resolution (SR) performance
in the past few years. However, being supervised, these SR methods are
restricted to specific training data, where the acquisition of the
low-resolution (LR) images from their high-resolution (HR) counterparts is
predetermined (e.g., bicubic downscaling), without any distracting artifacts
(e.g., sensor noise, image compression, non-ideal PSF, etc). Real LR images,
however, rarely obey these restrictions, resulting in poor SR results by SotA
(State of the Art) methods. In this paper we introduce "Zero-Shot" SR, which
exploits the power of Deep Learning, but does not rely on prior training. We
exploit the internal recurrence of information inside a single image, and train
a small image-specific CNN at test time, on examples extracted solely from the
input image itself. As such, it can adapt itself to different settings per
image. This allows to perform SR of real old photos, noisy images, biological
data, and other images where the acquisition process is unknown or non-ideal.
On such images, our method outperforms SotA CNN-based SR methods, as well as
previous unsupervised SR methods. To the best of our knowledge, this is the
first unsupervised CNN-based SR method
A Joint Intensity and Depth Co-Sparse Analysis Model for Depth Map Super-Resolution
High-resolution depth maps can be inferred from low-resolution depth
measurements and an additional high-resolution intensity image of the same
scene. To that end, we introduce a bimodal co-sparse analysis model, which is
able to capture the interdependency of registered intensity and depth
information. This model is based on the assumption that the co-supports of
corresponding bimodal image structures are aligned when computed by a suitable
pair of analysis operators. No analytic form of such operators exist and we
propose a method for learning them from a set of registered training signals.
This learning process is done offline and returns a bimodal analysis operator
that is universally applicable to natural scenes. We use this to exploit the
bimodal co-sparse analysis model as a prior for solving inverse problems, which
leads to an efficient algorithm for depth map super-resolution.Comment: 13 pages, 4 figure
Seven ways to improve example-based single image super resolution
In this paper we present seven techniques that everybody should know to
improve example-based single image super resolution (SR): 1) augmentation of
data, 2) use of large dictionaries with efficient search structures, 3)
cascading, 4) image self-similarities, 5) back projection refinement, 6)
enhanced prediction by consistency check, and 7) context reasoning. We validate
our seven techniques on standard SR benchmarks (i.e. Set5, Set14, B100) and
methods (i.e. A+, SRCNN, ANR, Zeyde, Yang) and achieve substantial
improvements.The techniques are widely applicable and require no changes or
only minor adjustments of the SR methods. Moreover, our Improved A+ (IA) method
sets new state-of-the-art results outperforming A+ by up to 0.9dB on average
PSNR whilst maintaining a low time complexity.Comment: 9 page
A Fully Progressive Approach to Single-Image Super-Resolution
Recent deep learning approaches to single image super-resolution have
achieved impressive results in terms of traditional error measures and
perceptual quality. However, in each case it remains challenging to achieve
high quality results for large upsampling factors. To this end, we propose a
method (ProSR) that is progressive both in architecture and training: the
network upsamples an image in intermediate steps, while the learning process is
organized from easy to hard, as is done in curriculum learning. To obtain more
photorealistic results, we design a generative adversarial network (GAN), named
ProGanSR, that follows the same progressive multi-scale design principle. This
not only allows to scale well to high upsampling factors (e.g., 8x) but
constitutes a principled multi-scale approach that increases the reconstruction
quality for all upsampling factors simultaneously. In particular ProSR ranks
2nd in terms of SSIM and 4th in terms of PSNR in the NTIRE2018 SISR challenge
[34]. Compared to the top-ranking team, our model is marginally lower, but runs
5 times faster
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