Academic literature on the topic 'Lanczos resampling'

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Journal articles on the topic "Lanczos resampling"

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Kotera, Hiroaki. "Inverse-Scaled Lanczos Filtering for Image Sharpening." Color and Imaging Conference 2020, no. 28 (2020): 215–20. http://dx.doi.org/10.2352/issn.2169-2629.2020.28.34.

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The edge response in retinal image is the first step for human vision recognizing the outside world. A variety of receptive field models for describing the impulse response have been proposed. Which satisfies the uncertain principle? occupied the interest from a point of minimizing the product (Δx)(Δ w) both in spatial and spectral. Among the typical edge response models, finally Gabor function and 2nd. Gaussian Derivative GD2 remained as strong candidates. While famous D. Marr and R. Young support GD2, many vision researchers prefer Gabor. The retinal edge response model is used for image sharpening.<br/> Different from the conventional image sharpening filters, this paper proposes a novel image sharpening filter by modifying the Lanczos resampling filter. The Lanczos filter is used for image scaling to resize digital images. Usually it works to interpolate the discrete sampled points like as a kind of smoothing filter not as sharpening. The Lanczos kernel is given by the product of sampling Sinc function and the scaled Sinc function. The scaled Sinc function expanded by the scale "s" plays a role of window function. The author noticed that the inverse scaling of Lanczos window can be used not for smoothing but for sharpening filter.<br/> This paper demonstrates how the proposed model works effectively in comparison with Gabor and GD2.
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Budzik, Grzegorz, and Paweł Turek. "THE IMPACT OF USE DIFFERENT TYPE OF IMAGE INTERPOLATION METHODS ON THE ACCURACY OF THE RECONSTRUCTION OF SKULL ANATOMICAL MODEL." Biomedical Engineering: Applications, Basis and Communications 32, no. 01 (2020): 2050008. http://dx.doi.org/10.4015/s1016237220500088.

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The paper presents comparative studies concerning the use different methods of image resampling on the accuracy of the reconstruction of oral and maxillofacial geometry. The study was conducted on 14 different patients. In order to extract the oral and maxillofacial models from DICOM data, a region-growing algorithm was used. Thresholds were set above 200 HU to select only craniofacial tissue. After an oral and maxillofacial tissue was segmented from DICOM data, a marching cubes algorithm was used for computing isosurfaces. Model with [Formula: see text][Formula: see text]mm voxel was chosen as the gold standard for models of [Formula: see text][Formula: see text]mm structure and improved with image resampling filters. In the study 7 different kernels were used allowing for filtration. The image resampling filters minimize maximum positive deviations, especially in the occipital, mandible and zygomatic bone area, and maximum negative deviations in the area of the maxilla and nasal bone. Lanczos filtering is the best method of interpolation as compared to other used methods, due to significantly increased visibility of the edges of the segmented structures. As a result of applying this method, partial volume effect artifact was minimized. The distributions and statistical parameters of resampled DICOM data prove that on this stage of data editing, it is possible to increase the accuracy of segmentation and reconstruction of the geometry.
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van Driel, M., L. Krischer, S. C. Stähler, K. Hosseini, and T. Nissen-Meyer. "Instaseis: instant global seismograms based on a broadband waveform database." Solid Earth 6, no. 2 (2015): 701–17. http://dx.doi.org/10.5194/se-6-701-2015.

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Abstract. We present a new method and implementation (Instaseis) to store global Green's functions in a database which allows for near-instantaneous (on the order of milliseconds) extraction of arbitrary seismograms. Using the axisymmetric spectral element method (AxiSEM), the generation of these databases, based on reciprocity of the Green's functions, is very efficient and is approximately half as expensive as a single AxiSEM forward run. Thus, this enables the computation of full databases at half the cost of the computation of seismograms for a single source in the previous scheme and allows to compute databases at the highest frequencies globally observed. By storing the basis coefficients of the numerical scheme (Lagrange polynomials), the Green's functions are 4th order accurate in space and the spatial discretization respects discontinuities in the velocity model exactly. High-order temporal interpolation using Lanczos resampling allows to retrieve seismograms at any sampling rate. AxiSEM is easily adaptable to arbitrary spherically symmetric models of Earth as well as other planets. In this paper, we present the basic rationale and details of the method as well as benchmarks and illustrate a variety of applications. The code is open source and available with extensive documentation at www.instaseis.net .
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van Driel, M., L. Krischer, S. C. Stähler, K. Hosseini, and T. Nissen-Meyer. "<i>Instaseis</i>: instant global seismograms based on a broadband waveform database." Solid Earth Discussions 7, no. 1 (2015): 957–1005. http://dx.doi.org/10.5194/sed-7-957-2015.

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Abstract. We present a new method and implementation (Instaseis) to store global Green's functions in a database which allows for near-instantaneous (on the order of milliseconds) extraction of arbitrary seismograms. Using the axisymmetric spectral element method (AxiSEM), the generation of these databases, based on reciprocity of the Green's functions, is very efficient and is approximately half as expensive as a single AxiSEM forward run. Thus, this enables the computation of full databases at half the cost of the computation of seismograms for a single source in the previous scheme and allows to compute databases at the highest frequencies globally observed. By storing the basis coefficients of the numerical scheme (Lagrange polynomials), the Green's functions are 4th order accurate in space and the spatial discretization respects discontinuities in the velocity model exactly. High order temporal interpolation using Lanczos resampling allows to retrieve seismograms at any sampling rate. AxiSEM is easily adaptable to arbitrary 1-D models or other spherical objects such as Mars. In this paper, we present the basic rationale and details of the method as well as benchmarks and illustrate a variety of applications. The code is open source and available with extensive documentation at http://www.instaseis.net.
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Mottola, Margherita, Stephan Ursprung, Leonardo Rundo, et al. "Reproducibility of CT-based radiomic features against image resampling and perturbations for tumour and healthy kidney in renal cancer patients." Scientific Reports 11, no. 1 (2021). http://dx.doi.org/10.1038/s41598-021-90985-y.

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AbstractComputed Tomography (CT) is widely used in oncology for morphological evaluation and diagnosis, commonly through visual assessments, often exploiting semi-automatic tools as well. Well-established automatic methods for quantitative imaging offer the opportunity to enrich the radiologist interpretation with a large number of radiomic features, which need to be highly reproducible to be used reliably in clinical practice. This study investigates feature reproducibility against noise, varying resolutions and segmentations (achieved by perturbing the regions of interest), in a CT dataset with heterogeneous voxel size of 98 renal cell carcinomas (RCCs) and 93 contralateral normal kidneys (CK). In particular, first order (FO) and second order texture features based on both 2D and 3D grey level co-occurrence matrices (GLCMs) were considered. Moreover, this study carries out a comparative analysis of three of the most commonly used interpolation methods, which need to be selected before any resampling procedure. Results showed that the Lanczos interpolation is the most effective at preserving original information in resampling, where the median slice resolution coupled with the native slice spacing allows the best reproducibility, with 94.6% and 87.7% of features, in RCC and CK, respectively. GLCMs show their maximum reproducibility when used at short distances.
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Dissertations / Theses on the topic "Lanczos resampling"

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Mezera, Lukáš. "Zlepšení rozlišení pro vícečetné snímky stejné scény." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2010. http://www.nusl.cz/ntk/nusl-237196.

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Úkolem této diplomové práce je navrhnout vlastní metodu pro zvýšení rozlišení v obraze scény, pokud je k dispozici více snímků dané scény. V teoretické části diplomové práce jsou jako nejlepší metody pro zvýšení rozlišení v obraze vybrány ty, které jsou založeny na principech zpracování signálu. Dále jsou popsány základní požadavky metod pro zvýšení rozlišení v obraze při přítomnosti více snímků stejné scény a jejich typická struktura. Následuje stručný přehled těchto metod a jejich vzájemné porovnání podle optimálních kritérií. Praktická část diplomové práce se zabývá samotným návrhem metody pro zvýšení rozlišení v obraze, pokud je k dispozici více snímků této scény. První navržená metoda je naimplementována a otestována. Při testování této metody je však  zjištěna její špatná funkčnost pro snímky scény s nízkým rozlišením, které vznikly vzájemnou rotací. Z toho důvodu je navržena vylepšená metoda pro zvýšení rozlišení v obraze. Tato metoda využívá při svém výpočtu robustních technik. Díky tomu je již vylepšená metoda nezávislá na rotaci mezi snímky scény s nízkým rozlišením. I tato metoda je řádně otestována a její výsledky jsou porovnány s výsledky první navržené metody pro zvýšení rozlišení v obraze. V porovnání výpočetních časů je lepší první navrhovaná metoda, avšak její výsledky pro obrazy obsahující rotace nejsou kvalitní. Oproti tomu pro obrazy, které vznikly pouze posunem při snímání scény, jsou tyto výsledky velice dobré. Vylepšená metoda je tedy využitelná zejména pro obrazy obsahující rotace. V závěru této práce je ještě navrženo jedno vylepšení, které by mohlo zlepšit výsledky druhé navrhnuté metody pro zvýšení rozlišení v obraze scény.
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Book chapters on the topic "Lanczos resampling"

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Madhukar, B. N., and R. Narendra. "Lanczos Resampling for the Digital Processing of Remotely Sensed Images." In Lecture Notes in Electrical Engineering. Springer India, 2013. http://dx.doi.org/10.1007/978-81-322-1524-0_48.

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