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The difference between a small and large Gaussian blur. In image processing, a Gaussian blur (also known as Gaussian smoothing) is the result of blurring an image by a Gaussian function (named after mathematician and scientist Carl Friedrich Gauss). It is a widely used effect in graphics software, typically to reduce image noise and reduce detail.
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.
Resize, crop, rotate, flip, JPEG lossless rotate/flip/crop, adjust exposure and colors etc., filters (sharpen, blur, average, emboss), batch convert, batch rename, edit IPTC info Free for non-commercial use Xv: View as ASCII or hex, magnify, determine pixel values
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.
Many image formats are native to one specific graphics application and are not offered as an export option in other software, due to proprietary considerations. An example of this is Adobe Photoshop 's native PSD-format (Prevention of Significant Deterioration), which cannot be opened in less sophisticated programs for image viewing or editing ...
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 the example images, the sizes of the Gaussian kernels employed to smooth the sample image were 10 pixels and 5 pixels.