2. Kernel filter. Write an image-processing library xernelfilees - Java that applies varions kemel fitherx to images, such as Gautrian Mbr, ahuper. Laplaciaw, emboss, and matlan Bhar, A kenel filter modifies the pivels in an image by replacing cach pixel with a linear combination of its aciphboring pixels. The matrix that characterizes the linear combination is known as the Aerwel. More specifically, to apply a kernel filter to a grapscale insuge, perform the following coeration for each picel F. - Align the center of the kemel on pixel p. - The new grayscale value of piuel p is obtained by multiplying each kemel element with the corresponding grayscale valoe, and adding the recults. To apply a kemel filter to a color image, perform the above eperative so the red, green, and blue components of cach pixel p separatcly, and combine the rosults. The following table describes the kernel filhers that you will implement and illustrates the results using a classic test image itbaboon snek To do so, implement the following public APL Here is some more information about the required behavior: Periodic boundary conditions. When applying a kernel filter to a pixel near the boundary, some of its neighboring pixels may not exist. In such cases, assume the leftmost column wraps around to the rightmost column and the top row wraps around to the bottom row (and vice versa). Rounding. When applying a kernel filter, the resulfing RGB components may becone fractional if the kernel weights are fractional. Round cach RGB component to the nearest integer, with ties rounding up. Clanping. When applying a kemel filter, the resulting RGB components may not remain between 0 and 255 . If an RGB component of a pixel is less than 0 , set it to 0 ; if is greater than 255 , set it to 255 . This mechanism for handling arithmetic overflow and underflow is known as clamping or saturating arithmetic. Test client. Your main() method should test each of your public methods. Performance requirement. All methods should take time proportional to the product of the number of pixels in the image and the number of elements in the kernel. Kernel filters are widely used for image processing in applications such as Photoshop or GIMP. They are also used in convolutional neural nehvorks to extract features from an image and in digital cameras to remove camera shake