1,556 research outputs found
CMISR: Circular Medical Image Super-Resolution
Classical methods of medical image super-resolution (MISR) utilize open-loop
architecture with implicit under-resolution (UR) unit and explicit
super-resolution (SR) unit. The UR unit can always be given, assumed, or
estimated, while the SR unit is elaborately designed according to various SR
algorithms. The closed-loop feedback mechanism is widely employed in current
MISR approaches and can efficiently improve their performance. The feedback
mechanism may be divided into two categories: local and global feedback.
Therefore, this paper proposes a global feedback-based closed-cycle framework,
circular MISR (CMISR), with unambiguous UR and SR elements. Mathematical model
and closed-loop equation of CMISR are built. Mathematical proof with
Taylor-series approximation indicates that CMISR has zero recovery error in
steady-state. In addition, CMISR holds plug-and-play characteristic which can
be established on any existing MISR algorithms. Five CMISR algorithms are
respectively proposed based on the state-of-the-art open-loop MISR algorithms.
Experimental results with three scale factors and on three open medical image
datasets show that CMISR is superior to MISR in reconstruction performance and
is particularly suited to medical images with strong edges or intense contrast
BLADE: Filter Learning for General Purpose Computational Photography
The Rapid and Accurate Image Super Resolution (RAISR) method of Romano,
Isidoro, and Milanfar is a computationally efficient image upscaling method
using a trained set of filters. We describe a generalization of RAISR, which we
name Best Linear Adaptive Enhancement (BLADE). This approach is a trainable
edge-adaptive filtering framework that is general, simple, computationally
efficient, and useful for a wide range of problems in computational
photography. We show applications to operations which may appear in a camera
pipeline including denoising, demosaicing, and stylization
Fast Deep Multi-patch Hierarchical Network for Nonhomogeneous Image Dehazing
Recently, CNN based end-to-end deep learning methods achieve superiority in
Image Dehazing but they tend to fail drastically in Non-homogeneous dehazing.
Apart from that, existing popular Multi-scale approaches are runtime intensive
and memory inefficient. In this context, we proposed a fast Deep Multi-patch
Hierarchical Network to restore Non-homogeneous hazed images by aggregating
features from multiple image patches from different spatial sections of the
hazed image with fewer number of network parameters. Our proposed method is
quite robust for different environments with various density of the haze or fog
in the scene and very lightweight as the total size of the model is around 21.7
MB. It also provides faster runtime compared to current multi-scale methods
with an average runtime of 0.0145s to process 1200x1600 HD quality image.
Finally, we show the superiority of this network on Dense Haze Removal to other
state-of-the-art models.Comment: CVPR Workshops Proceedings 202
Rethinking PRL: A Multiscale Progressively Residual Learning Network for Inverse Halftoning
Image inverse halftoning is a classic image restoration task, aiming to
recover continuous-tone images from halftone images with only bilevel pixels.
Because the halftone images lose much of the original image content, inverse
halftoning is a classic ill-problem. Although existing inverse halftoning
algorithms achieve good performance, their results lose image details and
features. Therefore, it is still a challenge to recover high-quality
continuous-tone images. In this paper, we propose an end-to-end multiscale
progressively residual learning network (MSPRL), which has a UNet architecture
and takes multiscale input images. To make full use of different input image
information, we design a shallow feature extraction module to capture similar
features between images of different scales. We systematically study the
performance of different methods and compare them with our proposed method. In
addition, we employ different training strategies to optimize the model, which
is important for optimizing the training process and improving performance.
Extensive experiments demonstrate that our MSPRL model obtains considerable
performance gains in detail restoration
Research and Education in Computational Science and Engineering
Over the past two decades the field of computational science and engineering
(CSE) has penetrated both basic and applied research in academia, industry, and
laboratories to advance discovery, optimize systems, support decision-makers,
and educate the scientific and engineering workforce. Informed by centuries of
theory and experiment, CSE performs computational experiments to answer
questions that neither theory nor experiment alone is equipped to answer. CSE
provides scientists and engineers of all persuasions with algorithmic
inventions and software systems that transcend disciplines and scales. Carried
on a wave of digital technology, CSE brings the power of parallelism to bear on
troves of data. Mathematics-based advanced computing has become a prevalent
means of discovery and innovation in essentially all areas of science,
engineering, technology, and society; and the CSE community is at the core of
this transformation. However, a combination of disruptive
developments---including the architectural complexity of extreme-scale
computing, the data revolution that engulfs the planet, and the specialization
required to follow the applications to new frontiers---is redefining the scope
and reach of the CSE endeavor. This report describes the rapid expansion of CSE
and the challenges to sustaining its bold advances. The report also presents
strategies and directions for CSE research and education for the next decade.Comment: Major revision, to appear in SIAM Revie
- …