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Deblurring an image using Wiener deconvolution. Deblurring is the process of removing blurring artifacts from images. Deblurring recovers a sharp image S from a blurred image B, where S is convolved with K (the blur kernel) to generate B. Mathematically, this can be represented as = (where * represents convolution).
Image enhancement techniques (like contrast stretching or de-blurring by a nearest neighbor procedure) provided by imaging packages use no a priori model of the process that created the image. With image enhancement noise can effectively be removed by sacrificing some resolution, but this is not acceptable in many applications.
Thanks to the Tensor G2 chipset in the Pixel 7 and Pixel 7 Pro, your blurry photos can now be fixed in Google Photos. Here's how to use the Photo Unblur feature.
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.
Thus, if the resulting enlarged image is recorded on contrasty photographic paper, the partial cancellation emphasizes the high-spatial-frequency information (fine detail) in the original, without loss of highlight or shadow detail. The resulting print appears more acute than one made without the unsharp mask: its acutance is increased.
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.
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