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Image scaling

About: Image scaling is a research topic. Over the lifetime, 3541 publications have been published within this topic receiving 50108 citations. The topic is also known as: upscaling & downscaling.


Papers
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Journal ArticleDOI
TL;DR: In this paper, the authors compared the performance of linear and cubic B-spline interpolation, linear interpolation and high-resolution cubic spline with edge enhancement with respect to the initial coordinate system.
Abstract: When resampling an image to a new set of coordinates (for example, when rotating an image), there is often a noticeable loss in image quality. To preserve image quality, the interpolating function used for the resampling should be an ideal low-pass filter. To determine which limited extent convolving functions would provide the best interpolation, five functions were compared: A) nearest neighbor, B) linear, C) cubic B-spline, D) high-resolution cubic spline with edge enhancement (a = -1), and E) high-resolution cubic spline (a = -0.5). The functions which extend over four picture elements (C, D, E) were shown to have a better frequency response than those which extend over one (A) or two (B) pixels. The nearest neighbor function shifted the image up to one-half a pixel. Linear and cubic B-spline interpolation tended to smooth the image. The best response was obtained with the high-resolution cubic spline functions. The location of the resampled points with respect to the initial coordinate system has a dramatic effect on the response of the sampled interpolating function?the data are exactly reproduced when the points are aligned, and the response has the most smoothing when the resampled points are equidistant from the original coordinate points. Thus, at the expense of some increase in computing time, image quality can be improved by resampled using the high-resolution cubic spline function as compared to the nearest neighbor, linear, or cubic B-spline functions.

844 citations

Journal ArticleDOI
TL;DR: The proposed technique would also allow precise coregistration of images for the measurement of surface displacements due to ice-flow or geomorphic processes, or for any other change detection applications.
Abstract: We describe a procedure to accurately measure ground deformations from optical satellite images. Precise orthorectification is obtained owing to an optimized model of the imaging system, where look directions are linearly corrected to compensate for attitude drifts, and sensor orientation uncertainties are accounted for. We introduce a new computation of the inverse projection matrices for which a rigorous resampling is proposed. The irregular resampling problem is explicitly addressed to avoid introducing aliasing in the ortho-rectified images. Image registration and correlation is achieved with a new iterative unbiased processor that estimates the phase plane in the Fourier domain for subpixel shift detection. Without using supplementary data, raw images are wrapped onto the digital elevation model and coregistered with a 1/50 pixel accuracy. The procedure applies to images from any pushbroom imaging system. We analyze its performance using Satellite pour l'Observation de la Terre (SPOT) images in the case of a null test (no coseismic deformation) and in the case of large coseismic deformations due to the Mw 7.1 Hector Mine, California, earthquake of 1999. The proposed technique would also allow precise coregistration of images for the measurement of surface displacements due to ice-flow or geomorphic processes, or for any other change detection applications. A complete software package, the Coregistration of Optically Sensed Images and Correlation, is available for download from the Caltech Tectonics Observatory website

777 citations

Journal ArticleDOI
TL;DR: In this paper, an iterative method for particle image velocimetry (PIV) was proposed to enhance the precision and spatial resolution of numerical interrogation schemes by taking into account the local velocity derivatives in order to increase the interrogation spatial density and a refinement of the local interrogation window size.
Abstract: Image processing methods for particle image velocimetry (PIV) are reviewed. The discussion focuses on iterative methods aimed at enhancing the precision and spatial resolution of numerical interrogation schemes. Emphasis is placed on the efforts made to overcome the limitations of the correlation interrogation method with respect to typical problems such as in-plane loss of pairs, velocity gradient compensation and correlation peak locking. The discussion shows how the correlation signal benefits from simple operations such as the window-offset technique, or the continuous window deformation, which compensates for the in-plane velocity gradient. The image interrogation process is presented within the discussion of the image matching problem and several algorithms and implementations currently in use are classified depending on the choice made about the particle image pattern matching scheme. Several methods that differ in their implementations are found to be substantially similar. Iterative image deformation methods that account for the continuous particle image pattern transformation are analysed and the effect of crucial choices such as image interpolation method, displacement prediction correction and correlation peak fit scheme are discussed. The quantitative performance assessment made through synthetic PIV images yields order-of-magnitude improvement on the precision of the particle image displacement at a sub-pixel level when the image deformation is applied. Moreover, the issue of spatial resolution is addressed and the limiting factors of the specific interrogation methods are discussed. Finally, an attempt for a flow-adaptive spatial resolution method is proposed. The method takes into account the local velocity derivatives in order to perform a local increase of the interrogation spatial density and a refinement of the local interrogation window size. The resulting spatial resolution is selectively enhanced. The method's performance is analysed and compared with some precursor techniques, namely the conventional cross-correlation analysis with and without the effect of a window discrete offset and deformation. The suitability of the method for the measurement in turbulent flows is illustrated with the application to a turbulent backward facing step flow.

776 citations

Proceedings ArticleDOI
29 Jul 2007
TL;DR: A new method for upsampling images which is capable of generating sharp edges with reduced input-resolution grid-related artifacts, based on a statistical edge dependency relating certain edge features of two different resolutions, which is generically exhibited by real-world images.
Abstract: In this paper we propose a new method for upsampling images which is capable of generating sharp edges with reduced input-resolution grid-related artifacts. The method is based on a statistical edge dependency relating certain edge features of two different resolutions, which is generically exhibited by real-world images. While other solutions assume some form of smoothness, we rely on this distinctive edge dependency as our prior knowledge in order to increase image resolution. In addition to this relation we require that intensities are conserved; the output image must be identical to the input image when downsampled to the original resolution. Altogether the method consists of solving a constrained optimization problem, attempting to impose the correct edge relation and conserve local intensities with respect to the low-resolution input image. Results demonstrate the visual importance of having such edge features properly matched, and the method's capability to produce images in which sharp edges are successfully reconstructed.

480 citations

Journal ArticleDOI
TL;DR: The purpose of this article is to provide a nuts and bolts procedure for calculating scale factors used for reconstructing images directly in SNR units and to validate the method for SNR measurement with phantom data.
Abstract: The method for phased array image reconstruction of uniform noise images may be used in conjunction with proper image scaling as a means of reconstructing images directly in SNR units. This facilitates accurate and precise SNR measurement on a per pixel basis. This method is applicable to root-sum-of-squares magnitude combining, B(1)-weighted combining, and parallel imaging such as SENSE. A procedure for image reconstruction and scaling is presented, and the method for SNR measurement is validated with phantom data. Alternative methods that rely on noise only regions are not appropriate for parallel imaging where the noise level is highly variable across the field-of-view. The purpose of this article is to provide a nuts and bolts procedure for calculating scale factors used for reconstructing images directly in SNR units. The procedure includes scaling for noise equivalent bandwidth of digital receivers, FFTs and associated window functions (raw data filters), and array combining.

479 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202323
202259
202175
2020105
2019104
2018126