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In image processing, a kernel, convolution matrix, or mask is a small matrix used for blurring, sharpening, embossing, edge detection, and more. This is accomplished by doing a convolution between the kernel and an image. Or more simply, when each pixel in the output image is a function of the nearby pixels (including itself) in the input image ...
Kernel (image processing), a matrix used for image convolution; Compute kernel, in GPGPU programming; Kernel method, in machine learning; Kernelization, a technique for designing efficient algorithms Kernel, a routine that is executed in a vectorized loop, for example in general-purpose computing on graphics processing units
Digital image processing is the application of signal processing techniques to the domain of images — two-dimensional signals such as photographs or video.Image processing does typically involve filtering or enhancing an image using various types of functions in addition to other techniques to extract information from the images.
For image processing, deconvolution is the process of approximately inverting the process that caused an image to be blurred. Specifically, unsharp masking is a simple linear image operation—a convolution by a kernel that is the Dirac delta minus a gaussian blur kernel.
Image derivatives can be computed by using small convolution filters of size 2 × 2 or 3 × 3, such as the Laplacian, Sobel, Roberts and Prewitt operators. [1] However, a larger mask will generally give a better approximation of the derivative and examples of such filters are Gaussian derivatives [ 2 ] and Gabor filters . [ 3 ]
Digital image processing is the use of a digital computer to process digital images through an algorithm. [1] [2] ... Kernel or mask Example Original Image ] ...
A major drawback to application of the algorithm is an inherent reduction in overall image contrast produced by the operation. [1] When utilized for image enhancement, the difference of Gaussians algorithm is typically applied when the size ratio of kernel (2) to kernel (1) is 4:1 or 5:1.
In applied mathematics, a steerable filter [1] is an orientation-selective convolution kernel used for image enhancement and feature extraction that can be expressed via a linear combination of a small set of rotated versions of itself. As an example, the oriented first derivative of a 2D Gaussian is a steerable filter.