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| 2. |
- Lindblad, Joakim, et al.
(author)
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De-noising of SRµCT Fiber Images by Total Variation Minimization
- 2010
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In: Proceedings of the 20th International Conference on Pattern Recognition (ICPR10). - Istanbul, Turkey. - 1051-4651. - 9781424475421 ; , s. 4621-4624
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Conference paper (peer-reviewed)abstract
- SRμCT images of paper and pulp fiber materials are characterized by a low signal to noise ratio. De-noising is therefore a common preprocessing step before segmentation into fiber and background components. We suggest a de-noising method based on total variation minimization using a modified Spectral Conjugate Gradient algorithm. Quantitative evaluation performed on synthetic 3D data and qualitative evaluation on real 3D paper fiber data confirm appropriateness of the suggested method for the particular application.
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| 3. |
- Lindblad, Joakim, et al.
(author)
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Defuzzification by Feature Distance Minimization Based on DC Programming
- 2007
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In: 5th International Symposium on Image and Signal Processing and Analysis, 2007. - 9789531841160 ; , s. 373-378
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Conference paper (peer-reviewed)abstract
- We introduce the use of DC programming, in combination with convex-concave regularization, as a deterministic approach for solving the optimization problem imposed by defuzzification by feature distance minimization. We provide a DC based algorithm for finding a solution to the defuzzification problem by expressing the objective function as a difference of two convex functions and iteratively solving a family of DC programs. We compare the performance with the previously recommended method, simulated annealing, on a number of test images. Encouraging results, together with several advantages of the DC based method, approve use of this approach, and motivate its further exploration.
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| 4. |
- Lindblad, Joakim, et al.
(author)
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Feature Based Defuzzification in Z² and Z³ Using a Scale Space Approach
- 2006
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In: Discrete Geometry for Computer Imagery 13th International Conference, DGCI 2006, Szeged, Hungary, October 25-27, 2006. Proceedings. - Berlin, Heidelberg : Springer Berlin Heidelberg. - 9783540476511 ; , s. 379-390
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Conference paper (peer-reviewed)abstract
- A defuzzification method based on feature distance minimization is further improved by incorporating into the distance function feature values measured on object representations at different scales. It is noticed that such an approach can improve defuzzification results by better preserving the properties of a fuzzy set; area preservation at scales in-between local (pixel-size) and global (the whole object) provides that characteristics of the fuzzy object are more appropriately exhibited in the defuzzification. For the purpose of comparing sets of different resolution, we propose a feature vector representation of a (fuzzy and crisp) set, utilizing a resolution pyramid. The distance measure is accordingly adjusted. The defuzzification method is extended to the 3D case. Illustrative examples are given.
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| 5. |
- Lukic, Tibor, et al.
(author)
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Deterministic Defuzzification based on Spectral Projected Gradient Optimization
- 2008
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In: 30th Symposium of the German Association for Pattern Recognition (DAGM). - Berlin / Heidelberg : Springer. - 9783540693208 ; , s. 476-485
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Conference paper (peer-reviewed)abstract
- We apply deterministic optimization based on Spectral Projected Gradient method in combination with concave regularization to solve the minimization problem imposed by defuzzification by feature distance minimization. We compare the performance of the proposed algorithm with the methods previously recommended for the same task, (non-deterministic) simulated annealing and (deterministic) DC based algorithm. The evaluation, including numerical tests performed on synthetic and real images, shows advantages of the new method in terms of speed and flexibility regarding inclusion of additional features in defuzzification. Its relatively low memory requirements allow the application of the suggested method for defuzzification of 3D objects.
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| 6. |
- Lukic, Tibor, et al.
(author)
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Regularized image denoising based on spectral gradient optimization
- 2011
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In: Inverse Problems. - : IOP Publishing. - 0266-5611 .- 1361-6420. ; 27:8, s. 085010:1-17
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Journal article (peer-reviewed)abstract
- Image restoration methods, such as denoising, deblurring, inpainting, etc, are often based on the minimization of an appropriately defined energy function. We consider energy functions for image denoising which combine a quadratic data-fidelity term and a regularization term, where the properties of the latter are determined by a used potential function. Many potential functions are suggested for different purposes in the literature. We compare the denoising performance achieved by ten different potential functions. Several methods for efficient minimization of regularized energy functions exist. Most are only applicable to particular choices of potential functions, however. To enable a comparison of all the observed potential functions, we propose to minimize the objective function using a spectral gradient approach; spectral gradient methods put very weak restrictions on the used potential function. We present and evaluate the performance of one spectral conjugate gradient and one cyclic spectral gradient algorithm, and conclude from experiments that both are well suited for the task. We compare the performance with three total variation-based state-of-the-art methods for image denoising. From the empirical evaluation, we conclude that denoising using the Huber potential (for images degraded by higher levels of noise; signal-to-noise ratio below 10 dB) and the Geman and McClure potential (for less noisy images), in combination with the spectral conjugate gradient minimization algorithm, shows the overall best performance.
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