1,686 research outputs found
Graph Spectral Image Processing
Recent advent of graph signal processing (GSP) has spurred intensive studies
of signals that live naturally on irregular data kernels described by graphs
(e.g., social networks, wireless sensor networks). Though a digital image
contains pixels that reside on a regularly sampled 2D grid, if one can design
an appropriate underlying graph connecting pixels with weights that reflect the
image structure, then one can interpret the image (or image patch) as a signal
on a graph, and apply GSP tools for processing and analysis of the signal in
graph spectral domain. In this article, we overview recent graph spectral
techniques in GSP specifically for image / video processing. The topics covered
include image compression, image restoration, image filtering and image
segmentation
Visual Quality Assessment and Blur Detection Based on the Transform of Gradient Magnitudes
abstract: Digital imaging and image processing technologies have revolutionized the way in which
we capture, store, receive, view, utilize, and share images. In image-based applications,
through different processing stages (e.g., acquisition, compression, and transmission), images
are subjected to different types of distortions which degrade their visual quality. Image
Quality Assessment (IQA) attempts to use computational models to automatically evaluate
and estimate the image quality in accordance with subjective evaluations. Moreover, with
the fast development of computer vision techniques, it is important in practice to extract
and understand the information contained in blurred images or regions.
The work in this dissertation focuses on reduced-reference visual quality assessment of
images and textures, as well as perceptual-based spatially-varying blur detection.
A training-free low-cost Reduced-Reference IQA (RRIQA) method is proposed. The
proposed method requires a very small number of reduced-reference (RR) features. Extensive
experiments performed on different benchmark databases demonstrate that the proposed
RRIQA method, delivers highly competitive performance as compared with the
state-of-the-art RRIQA models for both natural and texture images.
In the context of texture, the effect of texture granularity on the quality of synthesized
textures is studied. Moreover, two RR objective visual quality assessment methods that
quantify the perceived quality of synthesized textures are proposed. Performance evaluations
on two synthesized texture databases demonstrate that the proposed RR metrics outperforms
full-reference (FR), no-reference (NR), and RR state-of-the-art quality metrics in
predicting the perceived visual quality of the synthesized textures.
Last but not least, an effective approach to address the spatially-varying blur detection
problem from a single image without requiring any knowledge about the blur type, level,
or camera settings is proposed. The evaluations of the proposed approach on a diverse
sets of blurry images with different blur types, levels, and content demonstrate that the
proposed algorithm performs favorably against the state-of-the-art methods qualitatively
and quantitatively.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201
Light Field Denoising via Anisotropic Parallax Analysis in a CNN Framework
Light field (LF) cameras provide perspective information of scenes by taking
directional measurements of the focusing light rays. The raw outputs are
usually dark with additive camera noise, which impedes subsequent processing
and applications. We propose a novel LF denoising framework based on
anisotropic parallax analysis (APA). Two convolutional neural networks are
jointly designed for the task: first, the structural parallax synthesis network
predicts the parallax details for the entire LF based on a set of anisotropic
parallax features. These novel features can efficiently capture the high
frequency perspective components of a LF from noisy observations. Second, the
view-dependent detail compensation network restores non-Lambertian variation to
each LF view by involving view-specific spatial energies. Extensive experiments
show that the proposed APA LF denoiser provides a much better denoising
performance than state-of-the-art methods in terms of visual quality and in
preservation of parallax details
The application of visual saliency models in objective image quality assessment: a statistical evaluation
Advances in image quality assessment have shown the potential added value of including visual attention aspects in its objective assessment. Numerous models of visual saliency are implemented and integrated in different image quality metrics (IQMs), but the gain in reliability of the resulting IQMs varies to a large extent. The causes and the trends of this variation would be highly beneficial for further improvement of IQMs, but are not fully understood. In this paper, an exhaustive statistical evaluation is conducted to justify the added value of computational saliency in objective image quality assessment, using 20 state-of-the-art saliency models and 12 best-known IQMs. Quantitative results show that the difference in predicting human fixations between saliency models is sufficient to yield a significant difference in performance gain when adding these saliency models to IQMs. However, surprisingly, the extent to which an IQM can profit from adding a saliency model does not appear to have direct relevance to how well this saliency model can predict human fixations. Our statistical analysis provides useful guidance for applying saliency models in IQMs, in terms of the effect of saliency model dependence, IQM dependence, and image distortion dependence. The testbed and software are made publicly available to the research community
The fundamentals of unimodal palmprint authentication based on a biometric system: A review
Biometric system can be defined as the automated method of identifying or authenticating the identity of a living person based on physiological or behavioral traits. Palmprint biometric-based authentication has gained considerable attention in recent years. Globally, enterprises have been exploring biometric authorization for some time, for the purpose of security, payment processing, law enforcement CCTV systems, and even access to offices, buildings, and gyms via the entry doors. Palmprint biometric system can be divided into unimodal and multimodal. This paper will investigate the biometric system and provide a detailed overview of the palmprint technology with existing recognition approaches. Finally, we introduce a review of previous works based on a unimodal palmprint system using different databases
Sparse representation based hyperspectral image compression and classification
Abstract
This thesis presents a research work on applying sparse representation to lossy hyperspectral image
compression and hyperspectral image classification. The proposed lossy hyperspectral image
compression framework introduces two types of dictionaries distinguished by the terms sparse
representation spectral dictionary (SRSD) and multi-scale spectral dictionary (MSSD), respectively.
The former is learnt in the spectral domain to exploit the spectral correlations, and the
latter in wavelet multi-scale spectral domain to exploit both spatial and spectral correlations in
hyperspectral images. To alleviate the computational demand of dictionary learning, either a
base dictionary trained offline or an update of the base dictionary is employed in the compression
framework. The proposed compression method is evaluated in terms of different objective
metrics, and compared to selected state-of-the-art hyperspectral image compression schemes, including
JPEG 2000. The numerical results demonstrate the effectiveness and competitiveness of
both SRSD and MSSD approaches.
For the proposed hyperspectral image classification method, we utilize the sparse coefficients
for training support vector machine (SVM) and k-nearest neighbour (kNN) classifiers. In particular,
the discriminative character of the sparse coefficients is enhanced by incorporating contextual
information using local mean filters. The classification performance is evaluated and compared
to a number of similar or representative methods. The results show that our approach could outperform
other approaches based on SVM or sparse representation.
This thesis makes the following contributions. It provides a relatively thorough investigation
of applying sparse representation to lossy hyperspectral image compression. Specifically,
it reveals the effectiveness of sparse representation for the exploitation of spectral correlations
in hyperspectral images. In addition, we have shown that the discriminative character of sparse
coefficients can lead to superior performance in hyperspectral image classification.EM201
No-Reference Quality Assessment for 360-degree Images by Analysis of Multi-frequency Information and Local-global Naturalness
360-degree/omnidirectional images (OIs) have achieved remarkable attentions
due to the increasing applications of virtual reality (VR). Compared to
conventional 2D images, OIs can provide more immersive experience to consumers,
benefitting from the higher resolution and plentiful field of views (FoVs).
Moreover, observing OIs is usually in the head mounted display (HMD) without
references. Therefore, an efficient blind quality assessment method, which is
specifically designed for 360-degree images, is urgently desired. In this
paper, motivated by the characteristics of the human visual system (HVS) and
the viewing process of VR visual contents, we propose a novel and effective
no-reference omnidirectional image quality assessment (NR OIQA) algorithm by
Multi-Frequency Information and Local-Global Naturalness (MFILGN).
Specifically, inspired by the frequency-dependent property of visual cortex, we
first decompose the projected equirectangular projection (ERP) maps into
wavelet subbands. Then, the entropy intensities of low and high frequency
subbands are exploited to measure the multi-frequency information of OIs.
Besides, except for considering the global naturalness of ERP maps, owing to
the browsed FoVs, we extract the natural scene statistics features from each
viewport image as the measure of local naturalness. With the proposed
multi-frequency information measurement and local-global naturalness
measurement, we utilize support vector regression as the final image quality
regressor to train the quality evaluation model from visual quality-related
features to human ratings. To our knowledge, the proposed model is the first
no-reference quality assessment method for 360-degreee images that combines
multi-frequency information and image naturalness. Experimental results on two
publicly available OIQA databases demonstrate that our proposed MFILGN
outperforms state-of-the-art approaches
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