189 research outputs found
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
Antipodally invariant metrics for fast regression-based super-resolution
Dictionary-based super-resolution (SR) algorithms usually select dictionary atoms based on the distance or similarity metrics. Although the optimal selection of the nearest neighbors is of central importance for such methods, the impact of using proper metrics for SR has been overlooked in literature, mainly due to the vast usage of Euclidean distance. In this paper, we present a very fast regression-based algorithm, which builds on the densely populated anchored neighborhoods and sublinear search structures. We perform a study of the nature of the features commonly used for SR, observing that those features usually lie in the unitary hypersphere, where every point has a diametrically opposite one, i.e., its antipode, with same module and angle, but the opposite direction. Even though, we validate the benefits of using antipodally invariant metrics, most of the binary splits use Euclidean distance, which does not handle antipodes optimally. In order to benefit from both the worlds, we propose a simple yet effective antipodally invariant transform that can be easily included in the Euclidean distance calculation. We modify the original spherical hashing algorithm with this metric in our antipodally invariant spherical hashing scheme, obtaining the same performance as a pure antipodally invariant metric. We round up our contributions with a novel feature transform that obtains a better coarse approximation of the input image thanks to iterative backprojection. The performance of our method, which we named antipodally invariant SR, improves quality (Peak Signal to Noise Ratio) and it is faster than any other state-of-the-art method.Peer ReviewedPostprint (author's final draft
Locally-adapted convolution-based super-resolution of irregularly-sampled ocean remote sensing data
Super-resolution is a classical problem in image processing, with numerous
applications to remote sensing image enhancement. Here, we address the
super-resolution of irregularly-sampled remote sensing images. Using an optimal
interpolation as the low-resolution reconstruction, we explore locally-adapted
multimodal convolutional models and investigate different dictionary-based
decompositions, namely based on principal component analysis (PCA), sparse
priors and non-negativity constraints. We consider an application to the
reconstruction of sea surface height (SSH) fields from two information sources,
along-track altimeter data and sea surface temperature (SST) data. The reported
experiments demonstrate the relevance of the proposed model, especially
locally-adapted parametrizations with non-negativity constraints, to outperform
optimally-interpolated reconstructions.Comment: 4 pages, 3 figure
PredDiff: Explanations and Interactions from Conditional Expectations
PredDiff is a model-agnostic, local attribution method that is firmly rooted
in probability theory. Its simple intuition is to measure prediction changes
while marginalizing features. In this work, we clarify properties of PredDiff
and its connection to Shapley values. We stress important differences between
classification and regression, which require a specific treatment within both
formalisms. We extend PredDiff by introducing a new, well-founded measure for
interaction effects between arbitrary feature subsets. The study of interaction
effects represents an inevitable step towards a comprehensive understanding of
black-box models and is particularly important for science applications. As
opposed to Shapley values, our novel measure maintains the original linear
scaling and is thus generally applicable to real-world problems.Comment: 28 pages, 13 Figures, major revision (completeness relation, new
experiments, comparison to Shapley values), code available at
https://github.com/PredDiff/PredDiff202
Comparative Analysis of Building Insurance Prediction Using Some Machine Learning Algorithms
In finance and management, insurance is a product that tends to reduce or eliminate in totality or partially the loss caused due to different risks. Various factors affect house insurance claims, some of which contribute to formulating insurance policies including specific features that the house has. Machine Learning (ML) when brought into the field of insurance would enable seamless formulation of insurance policies with a better performance which will also save time. Various classification algorithms have been used since they have a long history and have also got some modifications for optimum functionality. To illustrate the performance of each of the ML algorithms that we used here, we analyzed an insurance dataset drawn from Zindi Africa competition which is said to be from Olusola Insurance Company in Lagos Nigeria. This study therefore, compares the performance of Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbor (KNN), Kernel Support Vector Machine (kSVM), NaĂŻve Bayes (NB), and Random Forest (RF) Regressors on a dataset got from Zindi.africa competition and their performances are checked using not only accuracy and precision metrics but also recall, and F1 score metrics, all displayed on the confusion matrix. The accuracy result shows that logistic regression and Kernel SVM both gave 78% but kSVM outperformed LR in precision with a percentage of 70.8% for kSVM and 64.8% for LR showing that kSVM offered the best result
Facial Texture Super-Resolution by Fitting 3D Face Models
This book proposes to solve the low-resolution (LR) facial analysis problem with 3D face super-resolution (FSR). A complete processing chain is presented towards effective 3D FSR in real world. To deal with the extreme challenges of incorporating 3D modeling under the ill-posed LR condition, a novel workflow coupling automatic localization of 2D facial feature points and 3D shape reconstruction is developed, leading to a robust pipeline for pose-invariant hallucination of the 3D facial texture
Anchoring The Cognitive Map To The Visual World
To interact rapidly and effectively with the environment, the mammalian brain needs a representation of the spatial layout of the external world (or a “cognitive map”). A person might need to know where she is standing to find her way home, for instance, or might need to know where she is looking to reach for her out-of-sight keys. For many behaviors, however, simply possessing a map is not enough; in order for a map to be useful in a dynamic world, it must be anchored to stable environmental cues. The goal of the present research is to address this spatial anchoring problem in two different domains: navigation and vision. In the first part of the thesis, which comprises Chapters 1-3, we examine how navigators use perceptual information to re-anchor their cognitive map after becoming lost, a process known as spatial reorientation. Using a novel behavioral paradigm with rodents, in Chapter 2 we show that the cognitive map is reoriented by dissociable inputs for identifying where one is and recovering which way one is facing. The findings presented in Chapter 2 also highlight the importance of environmental boundaries, such as the walls of a room, for anchoring the cognitive map. We thus predicted that there might exist a brain region that is selectively involved in boundary perception during navigation. Accordingly, in Chapter 3, we combine transcranial magnetic stimulation and virtual-reality navigation to reveal the existence of such a boundary perception region in humans. In the second part of this thesis, Chapter 4, we explore whether the same mechanisms that support the cognitive map of navigational space also mediate a map of visual space (i.e., where one is looking). Using functional magnetic resonance imaging and eye tracking, we show that human entorhinal cortex supports a map-like representation of visual space that obeys the same principles of boundary-anchoring previously observed in rodent maps of navigational space. Together, this research elucidates how mental maps are anchored to the world, thus allowing the mammalian brain to form durable spatial representations across body and eye movements
Medical Image Modality Synthesis and Resolution Enhancement Based on Machine Learning Techniques
To achieve satisfactory performance from automatic medical image analysis
algorithms such as registration or segmentation, medical imaging data with
the desired modality/contrast and high isotropic resolution are preferred, yet
they are not always available. We addressed this problem in this thesis using
1) image modality synthesis and 2) resolution enhancement.
The first contribution of this thesis is computed tomography (CT)-tomagnetic
resonance imaging (MRI) image synthesis method, which was developed
to provide MRI when CT is the only modality that is acquired. The
main challenges are that CT has poor contrast as well as high noise in soft
tissues and that the CT-to-MR mapping is highly nonlinear. To overcome these
challenges, we developed a convolutional neural network (CNN) which is a
modified U-net. With this deep network for synthesis, we developed the first
segmentation method that provides detailed grey matter anatomical labels on
CT neuroimages using synthetic MRI.
The second contribution is a method for resolution enhancement for a
common type of acquisition in clinical and research practice, one in which
there is high resolution (HR) in the in-plane directions and low resolution (LR)
in the through-plane direction. The challenge of improving the through-plane resolution for such acquisitions is that the state-of-art convolutional neural
network (CNN)-based super-resolution methods are sometimes not applicable
due to lack of external LR/HR paired training data. To address this challenge,
we developed a self super-resolution algorithm called SMORE and its iterative
version called iSMORE, which are CNN-based yet do not require LR/HR
paired training data other than the subject image itself. SMORE/iSMORE
create training data from the HR in-plane slices of the subject image itself, then
train and apply CNNs to through-plane slices to improve spatial resolution
and remove aliasing. In this thesis, we perform SMORE/iSMORE on multiple
simulated and real datasets to demonstrate their accuracy and generalizability.
Also, SMORE as a preprocessing step is shown to improve segmentation
accuracy.
In summary, CT-to-MR synthesis, SMORE, and iSMORE were demonstrated
in this thesis to be effective preprocessing algorithms for visual quality
and other automatic medical image analysis such as registration or segmentation
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