mpmendenhall wrote:
Given a convolution with no zeros in the fourier transform of the kernel (e.g. a gaussian blur), in theory there is exactly one unique source image that will produce the convolved one. In the limit of a computer that can carry out calculations to infinite precision, the original image can be perfectly restored.
This theory breaks down in real life with the introduction of noise, both the noise from real camera images and quantization/rounding noise in computer calculations.
Yeah, spherical cows in vacuum have no problems with inverting convolutions... :-)
A relevant point: how do you distinguish noise in the image from fine detail? Say, I have a picture of a gravel road, that variation in pixel RGB values that I observe, is that noise, or is that fine detail of gravel? How can you tell without prior knowledge of what (parts of) the image is *supposed* to look like?
When an algorithm enhances small-scale variation on the sky we call it noise because we know the sky is supposed to be more or less even locally. When an algorithm enhances similar variation in a far-off forest we call it fine detail of foliage because we know that forests are not smooth. But that is knowledge from outside of the image, algorithms don\'t have it.
You\'re saying that the noise in the image spoils the perfect deconvolution -- isn\'t exactly the same as saying that the fine detail in the image spoils the perfect deconvolution?
Mar 22, 2012 at 01:58 PM
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