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Glossary of Computer Vision Terms
Published in Edward R. Dougherty, Digital Image Processing Methods, 2020
Robert M. Haralick, Linda G. Shapiro
A linear spatial filter is a spatial filter for which the image intensity at coordinates (r, c) in the output image is some weighted average or linear combination of the image intensities located in a particular spatial pattern around coordinates (r, c) of the input image. A linear spatial filter is often used to change the spatial frequency characteristics of the image. For example, a linear spatial filter which emphasizes high spatial frequencies will tend to sharpen the edges in an image. A linear spatial filter which emphasizes low spatial firequencies will tend to blur the image and reduce salt and pepper noise. When the purpose of the filter is to enhance neighborhoods having certain shapes, the operation is sometimes called mask matching.
Parallel MR Image Reconstruction
Published in Joseph Suresh Paul, Raji Susan Mathew, Regularized Image Reconstruction in Parallel MRI with MATLAB®, 2019
Joseph Suresh Paul, Raji Susan Mathew
The central k-space region carries low spatial frequency information, and peripheral data carries high-frequency information required to improve the spatial resolution. The nominal spatial resolution of the image can be improved by extending the data collection farther from the k-space origin. Since a large chunk of the image information is contained in the low spatial frequencies, the addition of high spatial frequency information can only sharpen the image without affecting the contrast or the basic shape features [3]. This is illustrated in Figure 1.8 using k-space truncation with two square windows of different sizes. With a smaller square window used for truncation, the reconstructed image is blurred due to loss of high-frequency signals near the periphery of the k-space. However, with inclusion of higher frequencies using a larger window, the resolution of the reconstructed image is improved.
RF Metamaterials
Published in Filippo Capolino, Theory and Phenomena of Metamaterials, 2017
The performance of an imaging system is defined by the transfer function, which describes the (complex) transmission of the system as a function of the spatial frequency. The formula of [SSR+03] was used to calculate the transfer function using the predicted value of μ′′ = 0.14. This is shown as the dashed curve in Figure 14.12d. The transfer function was measured and the resulting points were also plotted in Figure 14.12d. It is clear that the actual value of μ′′ is rather larger than that estimated from measurements of a single element. A least-squares fitted value is μ′′ = 0.26, and the transfer function for this value is plotted as the full line in Figure 14.12d. The Rayleigh criterion was applied to estimate the resolution as ≈λ/64, although the measurements actually display a rather higher resolution, as indicated by the high spatial-frequency tail in Figure 14.12d.
Factors affecting the measurement resolution of super-resolution techniques based on speckle interferometry
Published in Journal of Modern Optics, 2022
In addition, because speckles were originally considered to be noise components [24], the specklegram calculated from the speckles before and after the lateral shift contains considerable noise, as shown in Figure 5(e). To remove this noise, it is necessary to use a filter to extract the phase component of the light [22]. The passband of this filter is also considered to affect the measurement results. Another factor that needs to be considered in the optical system is the area of the measurement object covered by a single pixel of the camera. If a single pixel covers a large area, the spatial frequency that each pixel can detect is expected to be low, and if it covers a small area, the spatial frequency is expected to be high. However, in this study, each pixel size of the image device was fixed at 1.6 × 1.6 µm, and the measured area per pixel was set by changing the lens magnification of the optical system during measurement.
Evaluating Luminance Uniformity Metrics Using Online Experiments
Published in LEUKOS, 2023
Belal Abboushi, Lia Irvin, Eduardo Rodriguez-Feo Bermudez, Michael Royer
The previously discussed metrics Max:Min, Avg:Min, CV, and EU are statistical and do not incorporate a term that accounts for how the human eye processes different contrast levels. The ability of the human eye to perceive complex patterns can be addressed by accounting for the spatial frequency of patterns and related contrast sensitivity (Ashdown 1996). The contrast sensitivity function (CSF) relates the visibility of a spatial pattern to its size and contrast (Dorr et al. 2017). Given that perceived uniformity describes the perceived evenness in luminance, quantifying perceived contrast provides insight into the detectability of luminance variations.
Improved Image Fusion of Colored and Grayscale Medical Images Based on Intuitionistic Fuzzy Sets
Published in Fuzzy Information and Engineering, 2018
Spatial Frequency: It is a measure of how often sinusoidal components of the structure repeat per unit of distance. In other words, it refers to the level of detail present in a stimulus per degree of visual angle.It can be computed as follows: where and SF of the fused image is high when its activity level is huge.Standard Deviation: Standard deviation comprises both original image and noise acquired during transmission. It is more powerful when there is no noise in the transmitted image and portraits the contrast of an image. STD can be calculated as follows: where Objective Image Fusion Performance Measure (OIFP): It is a measure for objectively assessing the pixel-level fusion performance [20]. This metric reflects the amount of edge and visual information obtained in a combined image from the input images to be fused, so that the performance of different image fusion algorithms can be compared. In this measure, a Sobel edge operator has been applied to images to calculate the edge strength and its orientation.In this work, to compute OIFP an open-source MATLAB program developed by [21] had been executed for source and output images.