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Flow Solutions
Published in Wen-Jei Yang, Handbook of Flow Visualization, 2018
Robert E. Smith, Robert A. Kudlinski
Bicubic interpolation is used to produce a smooth representation when there are relatively few points. Bilinear interpolation is faster and more appropriate when there is a large number of closely spaced points. A linear interpolation into a color-code table designates the color to be displayed at the pixel position. The color code for each pixel along a raster line is stored, and the process proceeds to the next raster line. After all the raster lines for a picture have been processed, the stored digital image is displayed on a CRT. Examples of flow-field visualization obtained with this technique are shown in Figs. 5 and 6. In Fig. 5, the display is the density about a shuttle-like configuration in a supersonic flow field. Also shown in Fig. 6 is the pressure distribution from a simulated firing of the shuttle solid-rocket booster.
Watermarking Attacks and Tools
Published in Frank Y. Shin, Digital Watermarking and Steganography, 2017
Sometimes when we scan a printed image or adjust its size for electronic publishing, image scaling may occur. This should be especially noted as we move increasingly in the direction of web publishing. An example is when the watermarked image is first scaled down (or down-sampled) by reducing both length and width by one-half—that is, by averaging every 2 × 2 block into a pixel. This quarter-sized image is then scaled up (or up-sampled) to its original size through the interpolation method. In general, bilinear interpolation will yield a smooth appearance, and bicubic interpolation will yield even smoother results. An example of the scaling attack is shown in Figure 5.7.
Image Processing Techniques in Remote Sensing
Published in Ni-Bin Chang, Kaixu Bai, Multisensor Data Fusion and Machine Learning for Environmental Remote Sensing, 2018
Mathematically, resampling involves interpolation and sampling to produce new estimates for pixels at different grids (Parker et al., 1983; Baboo and Devi, 2010). To date, a variety of methods have been developed for resampling, and the choice of resampling kernels is highly application-dependent. The three most common resampling kernels are nearest neighbor, bilinear interpolation, and cubic convolution. Nearest neighbor: Nearest neighbor is a method frequently used for resampling in remote sensing, which estimates a new value for each “corrected” pixel (i.e., new grid) using data values from the nearest “uncorrected” pixels (i.e., original grids). The advantages of nearest neighbor are its simplicity and capability to preserve original values in the unaltered scene. Nevertheless, the disadvantages of nearest neighbor are also significant, in particularly its blocky effects (Baboo and Devi, 2010). An example of image resampling with nearest neighbor is shown in Figure 4.3.Bilinear interpolation: Bilinear interpolation is an image smoothing method which uses only values from the four nearest pixels that are located in diagonal directions from a given pixel to estimate appropriate values of that pixel (Parker et al., 1983; Baboo and Devi, 2010). In general, bilinear interpolation takes a weighted average of the closest 2 × 2 neighborhood of known pixel values surrounding the corresponding pixel to produce an interpolated value. Weights assigned to the four pixel values are normally based on the computed pixel's distance (in 2D space) from each of the known points.Cubic convolution: Cubic convolution is conducted through a weighted average of 16 pixels nearby the corresponding input pixel through a cubic function. Compared to bilinear interpolation, cubic convolution performs better and the result does not have a disjointed appearance like nearest neighbor (Keys, 1981; Reichenbach and Geng, 2003). However, computational times required by cubic convolution are about 10 times more than those required by the nearest neighbor method (Baboo and Devi, 2010).
Improving weather dependent zone specific irrigation control scheme in IoT and big data enabled self driven precision agriculture mechanism
Published in Enterprise Information Systems, 2020
Bright Keswani, Ambarish G. Mohapatra, Poonam Keswani, Ashish Khanna, Deepak Gupta, Joel Rodrigues
Bilinear interpolation is basically an add-on of linear interpolation technique which is intended for interpolating functions of two variables (p, q) on a 2D grid pattern. The key element is that it executes linear interpolation in one way and on the other hand in another direction. Even though, each step is properly linear but the overall interpolation is not always linear. To understand the importance of the unknown function ‘f’ at the discrete point (p, q) then it is required to understand the worth of ‘f’ at four different points as shown in Equation 2.
Research on an ionospheric delay correction method for the interferometric imaging radar altimeter based on improved GIM data
Published in Journal of Spatial Science, 2021
Hongli Miao, Renchao Qu, Peng Mao, Xiangying Miao
The kriging interpolation method is also called spatial autocovariance optimal interpolation. It first considers the spatial distribution of the sample variation, determines the distance range within which the available data have an impact on the value of apoint to be interpolated, and then applies an optimal linear unbiased interpolation method. This method is asmooth interpolation method, and the results will have higher reliability when there are many data points that can be used; thus, it has unique advantages compared with bilinear interpolation (Carletti et al. 2000).
Semantic segmentation for remote sensing images based on an AD-HRNet model
Published in International Journal of Digital Earth, 2022
Xue Yang, Xiang Fan, Mingjun Peng, Qingfeng Guan, Luliang Tang
Dilated convolution is commonly used to replace regular convolution to extend the receptive field of convolutional kernel, which can obtain more effective information and improve the segmentation accuracy through a small amount of calculation during semantic segmentation (P. Wang et al. 2018). However, due to the existence of the dilated rate, the convolutional kernel is dispersed, which causes discontinuity in information acquisition and a serious grid effect. Bilinear interpolation (Gribbon and Bailey 2004) is a conventional upsampling method used by many semantic segmentation models including HRNet. The advantage of bilinear interpolation is that the calculation is simple and fast. Since bilinear interpolation predicts the pixel value of the sample point by several nearby pixels, it inevitably causes the loss of image details which reduces the segmentation accuracy. In this study, we construct an MDC-DUC module to replace the bilinear interpolation module in the original version of HRNet by considering both advantages of MDC (Mixed Dilated Convolution) and DUC (Dense Upsampling Convolution) blocks, as shown in Figure 3. In MDC-DUC module, the DUC block is applied to recover more missing detailed information. The proposed MDC block is embedded to the four parallel outputs of the original HRNet to expend the diversity of the receptive field, thus increasing the semantic accuracy at pixel-level, as shown in Figure 6. Specifically, the designed MDC block in this study is consisting of a dilated convolution with three different dilation rates (e.g. d = 1, 2, 5). The relevant formula for the dilated convolution is as follows. where is the size of the input convolutional kernel, is the dilated coefficient, and is equal to convolutional kernel size after dilated operation.