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Adaptive Regularization For Image Restoration Using A Variational Inequality Approach, Matthew Kitchener, Abdesselam Bouzerdoum, Son Lam Phung
Adaptive Regularization For Image Restoration Using A Variational Inequality Approach, Matthew Kitchener, Abdesselam Bouzerdoum, Son Lam Phung
Faculty of Informatics - Papers (Archive)
In this paper, a generalized image restoration method is formulated as a variational inequality problem, whose solution is obtained using a dynamic system approach. In this method, the restored image and the regularization parameter are obtained simultaneously. In particular, the optimum regularization parameter is determined adaptively, depending on noise and image content. The restoration problem is presented in a generalized form so that it maybe be implemented using different norms; only L1 and L2 norms have been implemented in this paper. A comparison based on experimental results shows that the proposed method achieves comparable if not better performance as some …
Automatic Parameter Selection For Feature-Enhanced Radar Image Restoration, Moeness G Amin, Cher Hau Seng, Son Lam Phung, Abdesselam Bouzerdoum
Automatic Parameter Selection For Feature-Enhanced Radar Image Restoration, Moeness G Amin, Cher Hau Seng, Son Lam Phung, Abdesselam Bouzerdoum
Faculty of Informatics - Papers (Archive)
In this paper, we propose a new technique for optimum parameter selection in non-quadratic radar image restoration. Although both the regularization hyper-parameter and the norm value are influential factors in the characteristics of the formed restoration, most existing optimization methods either require memory intensive computation or prior knowledge of the noise. Here, we present a contrast measure-based method for automated hyper-parameter selection. The proposed method is then extended to optimize the norm value used in non-quadratic image formation and restoration. The proposed method is evaluated on the MSTAR public target database and compared to the GCV method. Experimental results show …
A Compressive Sensing Approach To Image Restoration, Matthew Kitchener, Abdesselam Bouzerdoum, Son Lam Phung
A Compressive Sensing Approach To Image Restoration, Matthew Kitchener, Abdesselam Bouzerdoum, Son Lam Phung
Faculty of Informatics - Papers (Archive)
In this paper the image restoration problem is solved using a Compressive Sensing approach, and the translation invariant, a Trous, undecimated wavelet transform. The problem is cast as an unconstrained optimization problem which is solved using the Fletcher-Reeves nonlinear conjugate gradient method. A comparison based on experimental results shows that the proposed method achieves comparable if not better performance as other state-of-the-art techniques.
Fuzzy Logic-Based Image Fusion For Multi-View Through-The-Wall Radar, Cher Hau Seng, Abdesselam Bouzerdoum, Fok Hing Chi Tivive, Moeness G. Amin
Fuzzy Logic-Based Image Fusion For Multi-View Through-The-Wall Radar, Cher Hau Seng, Abdesselam Bouzerdoum, Fok Hing Chi Tivive, Moeness G. Amin
Faculty of Informatics - Papers (Archive)
In this paper, we propose a new technique for image fusion in multi-view through-the-wall radar imaging system. As most existing image fusion methods for through-the-wall radar imaging only consider a global fusion operator, it is desirable to consider the differences between each pixel using a local operator. Here, we present a fuzzy logic-based method for pixel-wise image fusion. The performance of the proposed method is evaluated on both simulated and real data from through-the-wall radar imaging system. Experimental results show that the proposed method yields improved performance, compared to existing methods.
A New Approach To Sparse Image Representation Using Mmv And K-Svd, Jie Yang, Abdesselam Bouzerdoum, Son Lam Phung
A New Approach To Sparse Image Representation Using Mmv And K-Svd, Jie Yang, Abdesselam Bouzerdoum, Son Lam Phung
Faculty of Informatics - Papers (Archive)
This paper addresses the problem of image representation based on a sparse decomposition over a learned dictionary. We propose an improved matching pursuit algorithm for Multiple Measurement Vectors (MMV) and an adaptive algorithm for dictionary learning based on multi-Singular Value Decomposition (SVD), and combine them for image representation. Compared with the traditional K-SVD and orthogonal matching pursuit MMV (OMPMMV) methods, the proposed method runs faster and achieves a higher overall reconstruction accuracy.
Face Detection Using Generalised Integral Image Features, Alister Cordiner, Philip Ogunbona, Wanqing Li
Face Detection Using Generalised Integral Image Features, Alister Cordiner, Philip Ogunbona, Wanqing Li
Faculty of Informatics - Papers (Archive)
This paper proposes generalised integral image features (GIIFs) for face detection. GIIFs provide a richer and more flexible set of features than Haar-like features. Due to the large set of possible GIIFs, a genetic algorithm is developed to select the feature space for the optimal weak classifiers. Experimental results have shown that this method is able to improve face detection accuracy.
Perceived Similarity And Visual Descriptions In Content-Based Image Retrieval, Yuan Zhong, Lei Ye, Wanqing Li, Philip Ogunbona
Perceived Similarity And Visual Descriptions In Content-Based Image Retrieval, Yuan Zhong, Lei Ye, Wanqing Li, Philip Ogunbona
Faculty of Informatics - Papers (Archive)
The use of low-level feature descriptors is pervasive in content-based image retrieval tasks and the answer to the question of how well these features describe users’ intention is inconclusive. In this paper we devise experiments to gauge the degree of alignment between the description of target images by humans and that implicitly provided by low-level image feature descriptors. Data was collected on how humans perceive similarity in images. Using images judged by humans to be similar, as ground truth, the performance of some MPEG-7 visual feature descriptors were evaluated. It is found that various descriptors play different roles in different …
Modelling Of Color Cross-Talk In Cmos Image Sensors, Wanqing Li, Philip Ogunbona, Yan Shi, Igor Kharitonenko
Modelling Of Color Cross-Talk In Cmos Image Sensors, Wanqing Li, Philip Ogunbona, Yan Shi, Igor Kharitonenko
Faculty of Informatics - Papers (Archive)
This paper presents a way to model the cross-talk effect in CMOS image sensors. Two algorithms are derived from the model; both of them work on the Bayer raw data and have low computational complexity. Experiments on Macbeth color chart and real images have shown the effectiveness of the modeling to eliminate the cross-talk effect and produce better quality images with traditional color interpolation and correction algorithms designed for CCD image sensors.
Image Compression Based On Genealogical Relation Of The Tsvq Indices, Jamshid Shanbehzadeh, Philip Ogunbona, Abdoihosein Sarafzadeh
Image Compression Based On Genealogical Relation Of The Tsvq Indices, Jamshid Shanbehzadeh, Philip Ogunbona, Abdoihosein Sarafzadeh
Faculty of Informatics - Papers (Archive)
The indices obtained by tree-structured vector quantisation (TSVQ) have an interesting property that enables them to give information about the correlation between two image blocks. Iftwo image blocks are highly correlated, they may have an identical index, or the same ancestors. The existence of high inter-block correlation in natural images results in having neighboring blocks with the same genealogy. This characteristic can be used to compress the indices. This paper introduces a novel method to exploit the genealogical relation between the image block indices obtained from a TSVQ. The performance of this scheme in terms of PSNR versus average rate …
Application Of Visual Modelling In Image Restoration And Colour Image Processing, Aziz Qureshi, Philip Ogunbona
Application Of Visual Modelling In Image Restoration And Colour Image Processing, Aziz Qureshi, Philip Ogunbona
Faculty of Informatics - Papers (Archive)
This paper describes the application of human visual models in (i) defining a visually uniform colour representation space and (ii) the formulation of visually weighted Kalman filtering for image restoration. The former being useful in colour image quantisation and compression. For (i), the uniformity of chromaticity differences at the ouptut of Frei ’s colour vision model [3] is tested and compensated for by using MacAdam’s uniform chromaticity space. For (ii), the dynamical image model of the Kalman filter is visually weighted using the frequency response of Stockham’s model [l] of human vision.
Shape Vq-Based Adaptive Predictive Lossless Image Coder, Jiazhao Wang, Philip Ogunbona, Golshah Naghdy
Shape Vq-Based Adaptive Predictive Lossless Image Coder, Jiazhao Wang, Philip Ogunbona, Golshah Naghdy
Faculty of Informatics - Papers (Archive)
A new shape adaptive predictive lossless image coder is proposed. Three classes of block shapes are delineated with associated “optimum” predctors. Each image is partitioned into sub-blocks that are classified into one of the three classes using vector quantisation. The encoder then employs the predictor corresponding to the class of the block under consideration. Performance evaluation of the proposed coder in comparison with four other lossless coders includmg lossless JPEG indicates its superiority.
Content-Based Retrieval From Compressed-Image Databases, Philip Ogunbona, P Sangassapaviriya
Content-Based Retrieval From Compressed-Image Databases, Philip Ogunbona, P Sangassapaviriya
Faculty of Informatics - Papers (Archive)
There is an enormous amount of multi-media data including images, video, speech, audio and text, distributed among the various computer nodes on the Internet. The extent to which a user wiU be able to derive useful information from these data depends largely on the ease with which required data can be retrieved from the databases. The share volume of the data also poses a storage constraint on the databases; hence these data will need to exist in the compressed form on the databases. In this paper we concentrate on image data and propose a new paradigm in which a compressed …
Edge Image Description Using Fractal Interpolation, P Motallebi, P O. Ogunbona
Edge Image Description Using Fractal Interpolation, P Motallebi, P O. Ogunbona
Faculty of Informatics - Papers (Archive)
Edge images derived from compressed image databases are described using fractal techniques. The proposed method is able to give affine transformation-invariant description suitable for use in a query-by-example database application. Comparison among the proposed method, polynomial interpolation and spline interpolation is given. It is concluded that fractal interpolation can give a compact description of image contours and is able to cope with random perturbation of the coordinates of the contour points by as much as 25 percent.
Similarity Measures For Compressed Image Databases, P Sangassapaviriya, Philip Ogunbona
Similarity Measures For Compressed Image Databases, P Sangassapaviriya, Philip Ogunbona
Faculty of Informatics - Papers (Archive)
For image database applications it is desirable that functions such as searching, browsing and partial recall be done without the need to totally decompress the image. This has the advantage of alleviating possible burden and degradation that the network may suffer. Edge images derived from wavelet-compressed images are considered as index that can be queried by example. Zernike moment invariants are used as descriptors for the index edge image and the query sketch image. The descriptions are compared for the purpose of database searching. The query images were allowed to undergo translation, rotation, scaling and some deformation. Simulation results gave …
Hybrid Predictive/Vq Lossless Image Coding, P O. Ogunbona, Jianli Wang, Golshah Naghdy
Hybrid Predictive/Vq Lossless Image Coding, P O. Ogunbona, Jianli Wang, Golshah Naghdy
Faculty of Informatics - Papers (Archive)
A multiplicative autoregressive model is used in a lossless predictive image coding scheme. The use of vector quantisation (VQ) for compression of the model coefficients leads to an improved compression ratio. Both image adaptive and universal codebooks are considered. A comparative analysis of the new coder is presented through simulation results.
New Feature-Based Image Adaptive Vector Quantisation Coder, Jamshid Shanbehzadeh, Philip O. Ogunbona
New Feature-Based Image Adaptive Vector Quantisation Coder, Jamshid Shanbehzadeh, Philip O. Ogunbona
Faculty of Informatics - Papers (Archive)
It is difficult to achieve a good low bit rate image compression performance with traditional block coding schemes such as transform coding and vector quantization, without regard for the human visual perception or signal dependency. These classical block coding schemes are based on minimizing the MSE at a certain rate. This procedure results in more bits being allocated to areas which may not be visually important and the resulting quantization noise manifests as a blocking artifact. Blocking artifacts are known to be psychologically more annoying than white noise when the human visual response is considered. While image adaptive vector quantization …