Actually, i read many research papers, where these terms kernels , image patch, and window is used in image denoising process. Is these terms have the same meaning or different?
The meaning of the terms that you mentioned especially the term kernel can change drastically from context.
But here I will give a definition most commonly used but be careful.
Kernel is a matrix that is convolved with the image as in the traditional 1D signal processing. Hence the kernel represents the 2-D impulse response of the system. The input of the system is an image and the output is the image convolved with the impulse response.
Image patch as the name suggests is a group of pixels in an image. Like if I had an image with 20 x 20 pixels. We can divide it into 1000 squares patches of size 2 x 2 pixels each.
Window is a similar concept as to the kernel.
These answers may not be general but will apply to most of the contexts.
Batch, Kernel, Window and Mask are same terminology in Image Processing.
Window size = 2n+1
if n = 0; window size is 1, then the processing is called as point processing, that is to perform pixel by pixel.
if n>1; for example n=1, window size is 3, then the processing is called as mask processing, it having neighborhood pixel.
Roughly yes, with such a kernel what happens is we do "direct convolution " .
https://ccrma.stanford.edu/~jos/sasp/FFT_versus_Direct_Convolution.html
In terms of computational complexity for filtering , there is a difference.
Kernels allow to map the data into a high dimensional feature space in
order to increase the computational power of linear machines.
You can read this:
http://www.kernel-machines.org/publications/pdfs/0701907.pdf
http://jmlr.csail.mit.edu/papers/volume2/genton01a/genton01a.pdf
Image patch and windows in image processing are used to get the local information by sub-dividing the images into number of blocks (may be overlapping or non-overlapping).
Kernel is a small matrix act as a transformation, it is used to map the original data to a modified data. using convolution kernels useful for blurring, sharpening, embossing, edge detection.
https://en.wikipedia.org/wiki/Kernel_(image_processing)
Image patching provides the ability to select arbitrary shaped regions on an image and replace them with a surface fit to other arbitrary shaped regions, together with an artificial noise component. This is an ideal way to remove unwanted defects from an image for cosmetic reasons. you can visit the link for more information http://ttic.uchicago.edu/~gregory/thesis/thesisChapter6.pdf
- Badges
- Science topic
- Similar topics
- Engineering
- Electrical Engineering
- Signal Processing
- Image Processing