publications: - title: "Robust Fusion of Irregularly Sampled Data Using Adaptive Normalized Convolution" author: - name: "Tuan Q. Pham" link: "http://www.cisra.com.au" - name: "Lucas J. van Vliet" link: "http://homepage.tudelft.nl/e3q6n/" - name: "Klamer Schutte" link: "https://researchr.org/alias/klamer-schutte" year: "2006" doi: "http://www.hindawi.com/journals/asp/2006/083268.abs.html" abstract: "We present a novel algorithm for image fusion from irregularly sampled data. The method is based on the framework of normalized convolution (NC), in which the local signal is approximated through a projection onto a subspace. The use of polynomial basis functions in this paper makes NC equivalent to a local Taylor series expansion. Unlike the traditional framework, however, the window function of adaptive NC is adapted to local linear structures. This leads to more samples of the same modality being gathered for the analysis, which in turn improves signal-to-noise ratio and reduces diffusion across discontinuities. A robust signal certainty is also adapted to the sample intensities to minimize the influence of outliers. Excellent fusion capability of adaptive NC is demonstrated through an application of super-resolution image reconstruction." links: doi: "http://www.hindawi.com/journals/asp/2006/083268.abs.html" "url": "http://www.hindawi.com/journals/asp/2006/083268.abs.html" tags: - "rule-based" - "application framework" - "analysis" - "data-flow" - "data-flow analysis" researchr: "https://researchr.org/publication/10.1155-ASP-2006-83268" cites: 0 citedby: 0 journal: "EURASIP Journal on Applied Signal Processing" volume: "2006" number: "83268" kind: "article" key: "10.1155-ASP-2006-83268" - title: "Performance of optimal registration estimators" author: - name: "Tuan Q. Pham" link: "http://www.cisra.com.au" - name: "Marijn Bezuijen" link: "https://researchr.org/alias/marijn-bezuijen" - name: "Lucas J. van Vliet" link: "http://homepage.tudelft.nl/e3q6n/" - name: "Klamer Schutte" link: "https://researchr.org/alias/klamer-schutte" - name: "Cris L. Luengo" link: "www.cb.uu.se/~cris/" year: "2005" doi: "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.78.9723" abstract: "This paper derives a theoretical limit for image registration and presents an iterative estimator that achieves the limit. The variance of any parametric registration is bounded by the Cramer-Rao bound (CRB). This bound is signal-dependent and is proportional to the variance of input noise. Since most available registration techniques are biased, they are not optimal. The bias, however, can be reduced to practically zero by an iterative gradientbased estimator. In the proximity of a solution, this estimator converges to the CRB with a quadratic rate. Images can be brought close to each other, thus speedup the registration process, by a coarse-to-fine multi-scale registration. The performance of iterative registration is finally shown to significantly increase image resolution from multiple low resolution images under translational motions." links: doi: "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.78.9723" researchr: "https://researchr.org/publication/Pham05performanceof" cites: 0 citedby: 0 booktitle: "Visual Information Processing XIV." kind: "inproceedings" key: "Pham05performanceof" - title: "Normalized Averaging Using Adaptive Applicability Functions with Applications in Image Reconstruction from Sparsely and Randomly Sampled Data" author: - name: "Tuan Q. Pham" link: "http://www.cisra.com.au" - name: "Lucas J. van Vliet" link: "http://homepage.tudelft.nl/e3q6n/" year: "2003" doi: "http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=2749&spage=485" abstract: "In this paper we describe a new strategy for using local structure adaptive filtering in normalized convolution. The shape of the filter, used as the applicability function in the context of normalized convolution, adapts to the local image structure and avoids filtering across borders. The size of the filter is also adaptable to the local sample density to avoid unnecessary smoothing over high certainty regions. We compared our adaptive interpolation technique with conventional normalized averaging methods. We found that our strategy yields a result that is much closer to the original signal both visually and in terms of MSE, meanwhile retaining sharpness and improving the SNR." links: doi: "http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=2749&spage=485" tags: - "context-aware" researchr: "https://researchr.org/publication/PhamV03" cites: 0 citedby: 0 pages: "485-492" booktitle: "scia" kind: "inproceedings" key: "PhamV03" - title: "Resolution enhancement of low-quality videos using a high-resolution frame" author: - name: "Tuan Q. Pham" link: "http://www.cisra.com.au" - name: "Lucas J. van Vliet" link: "http://homepage.tudelft.nl/e3q6n/" - name: "Klamer Schutte" link: "https://researchr.org/alias/klamer-schutte" year: "2006" doi: "http://dx.doi.org/10.1117/12.643489" abstract: "This paper proposes an example-based Super-Resolution (SR) algorithm of compressed videos in the Discrete Cosine Transform (DCT) domain. Input to the system is a Low-Resolution (LR) compressed video togetherwith a High-Resolution (HR) still image of similar content. Using a training set of corresponding LR-HR pairs of image patches from the HR still image, high-frequency details are transferred from the HR source to the LR video. The DCT-domain algorithm is much faster than example-based SR in spatial domain6 because of a reduction in search dimensionality, which is a direct result of the compact and uncorrelated DCT representation. Fast searching techniques like tree-structure vector quantization16 and coherence search1 are also key to the improved efficiency. Preliminary results on MJPEG sequence show promising result of the DCT-domain SR synthesis approach. " links: doi: "http://dx.doi.org/10.1117/12.643489" "url": "http://link.aip.org/link/?PSI/6077/607708/1" tags: - "rule-based" - "source-to-source" - "search" - "systematic-approach" - "open-source" researchr: "https://researchr.org/publication/pham%3A607708" cites: 0 citedby: 0 booktitle: "Visual Communications and Image Processing SPIE 6077" kind: "inproceedings" key: "pham:607708" - title: "Robust super-resolution by minimizing a Gaussian-weighted L2 error norm" author: - name: "Tuan Q. Pham" link: "http://www.cisra.com.au" - name: "Lucas J. van Vliet" link: "http://homepage.tudelft.nl/e3q6n/" - name: "Klamer Schutte" link: "https://researchr.org/alias/klamer-schutte" year: "2008" doi: "http://dx.doi.org/10.1088/1742-6596/124/1/012037" abstract: "Super-resolution restoration is the problem of restoring a high-resolution scene from multiple degraded low-resolution images under motion. Due to imaging blur and noise, this problem is ill-posed. Additional constraints such as smoothness of the solution via regularization is often required to obtain a stable solution. While adding a regularization term to the cost function is a standard practice in image restoration, we propose a restoration algorithm that does not require this extra regularization term. The robustness of the algorithm is achieved by a Gaussian-weighted L2 -norm in the data misfit term that does not response to intensity outliers. With the outliers suppressed, our solution behaves similarly to a maximum-likelihood solution in the presence of Gaussian noise. The effectiveness of our algorithm is demonstrated with super-resolution restoration of real infrared image sequences under severe aliasing and intensity outliers." links: doi: "http://dx.doi.org/10.1088/1742-6596/124/1/012037" "url": "http://stacks.iop.org/1742-6596/124/i=1/a=012037" tags: - "robust maximum likelihood" - "constraints" - "data-flow" researchr: "https://researchr.org/publication/1742-6596-124-1-012037" cites: 0 citedby: 0 journal: "Journal of Physics: Conference Series" volume: "124" number: "1" kind: "article" key: "1742-6596-124-1-012037" - title: "Separable bilateral filtering for fast video preprocessing" author: - name: "Tuan Q. Pham" link: "http://www.cisra.com.au" - name: "Lucas J. van Vliet" link: "http://homepage.tudelft.nl/e3q6n/" year: "2005" doi: "http://doi.ieeecomputersociety.org/10.1109/ICME.2005.1521458" abstract: "Bilateral filtering is an edge-preserving filtering technique that employs both geometric closeness and photometric similarity of neighboring pixels to construct its filter kernel. Multi-dimensional bilateral filtering is computationally expensive because the adaptive kernel has to be recomputed at every pixel. In this paper, we present a separable implementation of the bilateral filter. The separable implementation offers equivalent adaptive filtering capability at a fraction of execution time compared to the traditional filter. Because of this efficiency, the separable bilateral filter can be used for fast preprocessing of images and videos. Experiments show that better image quality and higher compression efficiency is achievable if the original video is preprocessed with the separable bilateral filter." links: doi: "http://doi.ieeecomputersociety.org/10.1109/ICME.2005.1521458" researchr: "https://researchr.org/publication/PhamV05" cites: 0 citedby: 0 pages: "454-457" booktitle: "icmcs" kind: "inproceedings" key: "PhamV05"