convolve

Each filter is convolved with a texture block to produce a response Rk (x, y): where is the texture block and * represents 2-D convolution.

At a convolution layer, the previous layer's feature maps are convolved with learnable kernels and put through the activation function to form the output feature map.

The slopes of the lines are equal to the standard deviations associated with the right and left Gaussian pdfs that have been convolved with the central Dj.

The relative difference of the solar irradiance convolved with a 1 nm triangular slit-function for the instrument given in the legend to the average solar irradiance of all the instruments given in the legend is shown in Fig.

The input artifact image is convolved with (3 x 3) mask weight to measure the difference in intensity along horizontal and vertical directions.

If an input signal that is not periodic with respect to the sampled data block is convolved with a suitable function in the time domain, it can be made to equal zero at the beginning and end of the data block.

The observed lineshapes are understood as a Lorentzian profile convolved with the thermal distribution of the ground state collision energies (6).

The obtained image is convolved with the Sobel operator The Sobel Operator is [12 1 0 0 0 -1 -2 -1]

The importance of this observation is that the shape of the motion blur kernel (which split into the rows of columns is a square convolved with a Gaussian) is preserved in the Cepstrum transform.

Then, it was convolved with the histogram to obtain the reduced-dimensional histogram.

Before the nodule is enhanced, the image should be convolved by the Gaussian function with the standard deviation [sigma], which not only eliminates the image noise but also improves the enhancement effect of the filter.

To compute DWT for a two-dimensional image, the original image is convolved along x and y directions by low pass and high pass filters as shown in Figure 1.

The input layer image (size: 262 x 262) is convolved with convolution kernel K1 (size: 7 x 7) to obtain C1 (size: 256 x 256) layer.

In the convolution layer, the input data is convolved with a learning convolution kernel and the result of convolution forms the characteristic pattern of this layer.