Abstract
Blurred image due to lens defocus can be simulated mathematically as a convolution result between sharp image and Point Spread Function of blur (unfocus) lens. Image sharpening is the opposite of image blurring which can be done by deconvolution. This research was conducted to simulate digital image sharpening using the Blind deconvolution method. The Point Spread Function of the lens effect is used as the deconvolution Kernel function. This Point Spread Function is modeled mathematically with the Gaussian function approach. Convolution results between digital images from photos of an object are convoluted with the point spread function to produce a blurry image. The blurry image is then sharpened using the Blind convolution method. The slight difference between the deconvolution result image and the original object photo image indicates that the program is running well. Peak Signal to Noise Ratio (PSNR) is used to determine image sharpening recovery. Optimal sharpening recovery from deconvolution iterations is obtained at the maximum PSNR value
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