convolution

Here C1 and C2 are convolutional layers, S1 and S2 are the down-sampling layers, and K1 and K2 are the convolution kernels. The network is a seven-layer CNN.

In LeNet-5 model, for example, the input size is 32*32 in C1 Layer, the convolution kernel's size is 5*5, and the size of C1 Layer is 28*28 (28=32-5+1).

Example of a 7x7 convolution kernel done using the two pass separable filter approach

Parameter k(s) denotes convolution kernel, and we utilize Hanning window function in this paper.

The matrix A is defined in terms of the convolution kernel

Conceptually, this kernel is a special case of convolution kernels [9], so we have the following definition of convolution kernel style.

It uses a convolution kernel that is the shape of a Gaussian hump or "bell-shaped".

Therefore the response operator R has a convolution structure with a convolution kernel c*[M.sup.i]b.

Since the input image is represented as a set of discrete pixels, we have to find a discrete convolution kernel that can approximate the second derivatives in the definition of the Laplacian [4-8].

A SF is an orientation-selective convolution kernel image processing algorithm put forward by Adelson and Freeman [20].

This paper presents a method for deblurring distorted images using a known convolution kernel. Pre-existing methods and algorithms are presented, each with its advantages and disadvantages.