214 research outputs found
Multi-modal dictionary learning for image separation with application in art investigation
In support of art investigation, we propose a new source separation method
that unmixes a single X-ray scan acquired from double-sided paintings. In this
problem, the X-ray signals to be separated have similar morphological
characteristics, which brings previous source separation methods to their
limits. Our solution is to use photographs taken from the front and back-side
of the panel to drive the separation process. The crux of our approach relies
on the coupling of the two imaging modalities (photographs and X-rays) using a
novel coupled dictionary learning framework able to capture both common and
disparate features across the modalities using parsimonious representations;
the common component models features shared by the multi-modal images, whereas
the innovation component captures modality-specific information. As such, our
model enables the formulation of appropriately regularized convex optimization
procedures that lead to the accurate separation of the X-rays. Our dictionary
learning framework can be tailored both to a single- and a multi-scale
framework, with the latter leading to a significant performance improvement.
Moreover, to improve further on the visual quality of the separated images, we
propose to train coupled dictionaries that ignore certain parts of the painting
corresponding to craquelure. Experimentation on synthetic and real data - taken
from digital acquisition of the Ghent Altarpiece (1432) - confirms the
superiority of our method against the state-of-the-art morphological component
analysis technique that uses either fixed or trained dictionaries to perform
image separation.Comment: submitted to IEEE Transactions on Images Processin
Development Of A High Performance Mosaicing And Super-Resolution Algorithm
In this dissertation, a high-performance mosaicing and super-resolution algorithm is described. The scale invariant feature transform (SIFT)-based mosaicing algorithm builds an initial mosaic which is iteratively updated by the robust super resolution algorithm to achieve the final high-resolution mosaic. Two different types of datasets are used for testing: high altitude balloon data and unmanned aerial vehicle data. To evaluate our algorithm, five performance metrics are employed: mean square error, peak signal to noise ratio, singular value decomposition, slope of reciprocal singular value curve, and cumulative probability of blur detection. Extensive testing shows that the proposed algorithm is effective in improving the captured aerial data and the performance metrics are accurate in quantifying the evaluation of the algorithm
Deep MR Brain Image Super-Resolution Using Spatio-Structural Priors
High resolution Magnetic Resonance (MR) images are desired for accurate
diagnostics. In practice, image resolution is restricted by factors like
hardware and processing constraints. Recently, deep learning methods have been
shown to produce compelling state-of-the-art results for image
enhancement/super-resolution. Paying particular attention to desired
hi-resolution MR image structure, we propose a new regularized network that
exploits image priors, namely a low-rank structure and a sharpness prior to
enhance deep MR image super-resolution (SR). Our contributions are then
incorporating these priors in an analytically tractable fashion \color{black}
as well as towards a novel prior guided network architecture that accomplishes
the super-resolution task. This is particularly challenging for the low rank
prior since the rank is not a differentiable function of the image matrix(and
hence the network parameters), an issue we address by pursuing differentiable
approximations of the rank. Sharpness is emphasized by the variance of the
Laplacian which we show can be implemented by a fixed feedback layer at the
output of the network. As a key extension, we modify the fixed feedback
(Laplacian) layer by learning a new set of training data driven filters that
are optimized for enhanced sharpness. Experiments performed on publicly
available MR brain image databases and comparisons against existing
state-of-the-art methods show that the proposed prior guided network offers
significant practical gains in terms of improved SNR/image quality measures.
Because our priors are on output images, the proposed method is versatile and
can be combined with a wide variety of existing network architectures to
further enhance their performance.Comment: Accepted to IEEE transactions on Image Processin
A novel robust reversible watermarking scheme for protecting authenticity and integrity of medical images
It is of great importance in telemedicine to protect authenticity and
integrity of medical images. They are mainly addressed by two technologies, which
are region of interest (ROI) lossless watermarking and reversible watermarking.
However, the former causes biases on diagnosis by distorting region of none interest
(RONI) and introduces security risks by segmenting image spatially for watermark
embedding. The latter fails to provide reliable recovery function for the tampered
areas when protecting image integrity. To address these issues, a novel robust
reversible watermarking scheme is proposed in this paper. In our scheme, a reversible
watermarking method is designed based on recursive dither modulation (RDM) to
avoid biases on diagnosis. In addition, RDM is combined with Slantlet transform and
singular value decomposition to provide a reliable solution for protecting image
authenticity. Moreover, ROI and RONI are divided for watermark generation to
design an effective recovery function under limited embedding capacity. Finally,
watermarks are embedded into whole medical images to avoid the risks caused by
segmenting image spatially. Experimental results demonstrate that our proposed
lossless scheme not only has remarkable imperceptibility and sufficient robustness,
but also provides reliable authentication, tamper detection, localization and recovery
functions, which outperforms existing schemes for protecting medical image
Noise Level Estimation for Digital Images Using Local Statistics and Its Applications to Noise Removal
In this paper, an automatic estimation of additive white Gaussian noise technique is proposed. This technique is built according to the local statistics of Gaussian noise. In the field of digital signal processing, estimation of the noise is considered as pivotal process that many signal processing tasks relies on. The main aim of this paper is to design a patch-based estimation technique in order to estimate the noise level in natural images and use it in blind image removal technique. The estimation processes is utilized selected patches which is most contaminated sub-pixels in the tested images sing principal component analysis (PCA). The performance of the suggested noise level estimation technique is shown its superior to state of the art noise estimation and noise removal algorithms, the proposed algorithm produces the best performance in most cases compared with the investigated techniques in terms of PSNR, IQI and the visual perception
Feature Extraction for image super-resolution using finite rate of innovation principles
To understand a real-world scene from several multiview pictures, it is necessary to find
the disparities existing between each pair of images so that they are correctly related to one
another. This process, called image registration, requires the extraction of some specific
information about the scene. This is achieved by taking features out of the acquired
images. Thus, the quality of the registration depends largely on the accuracy of the
extracted features.
Feature extraction can be formulated as a sampling problem for which perfect re-
construction of the desired features is wanted. The recent sampling theory for signals with
finite rate of innovation (FRI) and the B-spline theory offer an appropriate new frame-
work for the extraction of features in real images. This thesis first focuses on extending the
sampling theory for FRI signals to a multichannel case and then presents exact sampling
results for two different types of image features used for registration: moments and edges.
In the first part, it is shown that the geometric moments of an observed scene can
be retrieved exactly from sampled images and used as global features for registration. The
second part describes how edges can also be retrieved perfectly from sampled images for
registration purposes. The proposed feature extraction schemes therefore allow in theory
the exact registration of images. Indeed, various simulations show that the proposed
extraction/registration methods overcome traditional ones, especially at low-resolution.
These characteristics make such feature extraction techniques very appropriate for
applications like image super-resolution for which a very precise registration is needed. The
quality of the super-resolved images obtained using the proposed feature extraction meth-
ods is improved by comparison with other approaches. Finally, the notion of polyphase
components is used to adapt the image acquisition model to the characteristics of real
digital cameras in order to run super-resolution experiments on real images
NON-INVASIVE IMAGE DENOISING AND CONTRAST ENHANCEMENT TECHNIQUES FOR RETINAL FUNDUS IMAGES
The analysis of retinal vasculature in digital fundus images is important for
diagnosing eye related diseases. However, digital colour fundus images suffer from
low and varied contrast, and are also affected by noise, requiring the use of fundus
angiogram modality. The Fundus Fluorescein Angiogram (FFA) modality gives 5 to
6 time’s higher contrast. However, FFA is an invasive method that requires contrast
agents to be injected and this can lead other physiological problems. A reported
digital image enhancement technique named RETICA that combines Retinex and ICA
(Independent Component Analysis) techniques, reduces varied contrast, and enhances
the low contrast blood vessels of model fundus images
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