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Asked: 2026-05-24T15:42:25+00:00

I’m thinking of starting a project for school where I’ll use genetic algorithms to

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I’m thinking of starting a project for school where I’ll use genetic algorithms to optimize digital sharpening of images. I’ve been playing around with unsharp masking (USM) techniques in Photoshop. Basically, I want to create a software that optimizes the parameters (i.e. blur radius, types of blur, blending the image) to create the “best-fit” set of filters.

I’m sort of quickly planning this project before starting it, and I can’t think of a good fitness function for the ‘selection’ part. How would I determine the ‘quality’ of the filter sets, or measure how sharp the image is?

Also, I will be programming using python (with the Python Imaging Library) since it’s the only language I’m proficient with. Should I learn a low-level language instead?

Any advice/tips on anything is greatly appreciated. Thanks in advance!

tl;dr How do I measure how ‘sharp’ an image is?

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1 Answer

  1. Editorial Team

    if its for tuning parameters you could take a known image and apply a known blurring/low pass filter. Then sharpen this with your GA+USM algorithm. Calculate your fitness function making use of the original image, e.g maybe something as simple as the mean absolute error. May need to create different datasets, e.g. landscape images (mostly sharp, in focus with large depth of field), portrait images (could be large areas deliberately out of focus and “soft”), along with low noise and noisy images. Sharpening noisy images is actually quite a challenge.

    It would definitely be worth taking a look at Bruce Frasier’ work on sharpening techniques for Photoshop etc.

    Also it might worth checking out Imatest (www.imatest.com) to see if there is anything regarding sharpness/resolution. And finally you might also consider resolution charts.

    And finally I seroiusly doubt one set of ideal parameters exists for USM, the optimum parameters will be image dependant and indeed be a personal perference (thatwhy I suggest starting for a known sharp image and blurring it). Understanding the type of image is probably as important and in itself and very interesting and challenging problem. Although perhaps basic hueristics like image varinance and edge histogram would reveal suitable clues.

    Anyway just a thought, hopefully some of the above is useful

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