4,939 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
A knowledge based architecture for the virtual restoration of ancient photos
Historical images are essential documents of the recent past. Nevertheless, time and bad preservation
corrupt their physical supports. Digitization can be the solution to extend their \u201clives\u201d, and digital techniques
can be used to recover lost information. This task is often difficult and time-consuming, if commercial
restoration tools are used for the purpose. A new solution is proposed to help non-expert users
in restoring their damaged photos. First, we defined a dual taxonomy for the defects in printed and digitized
photos. We represented our restoration domain with an ontology and we created some rules to
suggest actions to perform in case of some specific events. Classes and properties of the ontology are
included into a knowledge base, that grows dynamically with its use. A prototypal tool and a web application
version have been implemented as an interface to the database, and to support non-expert users
in the restoration process
PSO Based Lossless and Robust Image Watermarking using Integer Wavelet Transform
In recent days, the advances in the broadcasting of multimedia contents in digital format motivate to protect this digital multimedia content form illegal use, such as manipulation, duplication and redistribution. However, watermarking algorithms are designed to meet the requirements of different applications, because, various applications have various requirements. This paper intends to design a new watermarking algorithm with an aim of provision of a tradeoff between the robustness and imperceptibility and also to reduce the information loss. This approach applies Integer Wavelet Transform (IWT) instead of conventional floating point wavelet transforms which are having main drawback of round of error. Then the most popular artificial intelligence technique, particle swarm optimization (PSO) used for optimization of watermarking strength. The strength of watermarking technique is directly related to the watermarking constant alpha. The PSO optimizes alpha values such that, the proposed approach achieves better robustness over various attacks and an also efficient imperceptibility. Numerous experiments are conducted over the proposed approach to evaluate the performance. The obtained experimental results demonstrates that the proposed approach is superior compared to conventional approach and is able to provide efficient resistance over Gaussian noise, sal
Coherence retrieval using trace regularization
The mutual intensity and its equivalent phase-space representations quantify
an optical field's state of coherence and are important tools in the study of
light propagation and dynamics, but they can only be estimated indirectly from
measurements through a process called coherence retrieval, otherwise known as
phase-space tomography. As practical considerations often rule out the
availability of a complete set of measurements, coherence retrieval is usually
a challenging high-dimensional ill-posed inverse problem. In this paper, we
propose a trace-regularized optimization model for coherence retrieval and a
provably-convergent adaptive accelerated proximal gradient algorithm for
solving the resulting problem. Applying our model and algorithm to both
simulated and experimental data, we demonstrate an improvement in
reconstruction quality over previous models as well as an increase in
convergence speed compared to existing first-order methods.Comment: 28 pages, 10 figures, accepted for publication in SIAM Journal on
Imaging Science
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