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Image-Processing Algorithms
Published in Junichi Nakamura, Image Sensors and Signal Processing for Digital Still Cameras, 2017
In the nearest neighbor interpolation, each interpolated pixel is assigned the value of the pixel value that is the nearest from the original data. Figure 8.20 shows nearest neighbor interpolation in two-dimensional space. In the figure, the interpolation result, Pi, has the same value as the original image pixel, P1. The interpolation function is shown in Equation 8.12. Nearest neighbor interpolation is the simplest method and requires little computational resource. The drawback is the poor quality of the interpolated image.
Digital Image Fundamentals
Published in Sheila Anand, L. Priya, A Guide for Machine Vision in Quality Control, 2019
In nearest-neighbor interpolation, image interpolation works in two directions and tries to achieve a best approximation of a pixel’s intensity based on the values at surrounding pixels. For example, each interpolated output pixel can be assigned the value of the nearest sample point in the input image. This is represented pictorially in Figure 2.21. This interpolation can be represented using the following equation:
Steganography Based on Interpolation and Edge Detection Techniques
Published in S. Ramakrishnan, Cryptographic and Information Security, 2018
While the nearest neighbor interpolation method is simple to implement, it often produces undesirable artifacts, such as the distortion of straight edges in images with a high resolution. Smoother results can be obtained by using more sophisticated techniques, such as cubic convolution interpolation. Yet, the price paid for smoother approximations is an additional computational burden.
Analysis of factors affecting the frequency of crashes on interstate freeways by vehicle type considering multiple weather variables
Published in Journal of Transportation Safety & Security, 2022
Cristopher Aguilar, Brendan J. Russo, Amin Mohebbi, Simin Akbariyeh
AZMET, operated by the University of Arizona, has meteorological stations throughout the state of Arizona (AZMET, 1987). These stations have a higher density near southern and central Arizona due to the proximity to the megacities of Phoenix and Tucson. The data collected in these stations are pointwise in the form of time series, which makes them not quite suitable for spatial analysis. Therefore, the data were interpolated spatially before they were downscaled to the Arizona interstates. Three different methods were used in spatial interpolation (De Smith, Goodchild, & Longley, 2007):Linear: The linear interpolation of a data set consists of continuous best fit lines between each pair of data. This method has a differentiability class of zero () which means every best fit line can have a different slope.Nearest neighbor: In the nearest neighbor interpolation, the unknown point of interest takes the nearest data set value. This method is popular in resampling grids or images, especially when the goal is to keep the grid or pixel values unchanged.Natural neighbor: In the natural neighbor interpolation, the unknown point of interest is calculated as the average of the adjacent points. The definition of the adjacency is based on the weight matrix typically is built spatially using the Voronoi decomposition (Voronoi, 1909). This method is prevalent in creating spatial averages for precipitation in hydrologic science.
Object detection of inland waterway ships based on improved SSD model
Published in Ships and Offshore Structures, 2023
Yang Yang, Pengyu Chen, Kaifa Ding, Zhuang Chen, Kaixuan Hu
As shown in Figure 6, the size of the feature map becomes larger after up-sampling, and the corresponding pixels of it and the feature map of the same size in down-sampling are added to complete the information fusion. To obtain the optimal fusion method, three representative up-sampling methods, nearest neighbour interpolation, bilinear interpolation and transposed convolution, are selected for comparison. Nearest neighbour interpolation
Colour filter array demosaicking: a brief survey
Published in The Imaging Science Journal, 2018
M. S. Safna Asiq, W. R. Sam Emmanuel
The nearest neighbour interpolation [29] uses the nearest known pixel value to identify the missing centre pixel. The interpolation is done in a certain order with respect to the nearest neighbouring pixel values. It assigns the value of the nearest point to each grid mode rather than distant pixel values to attain a good reconstructed image.