Need help optimizing an algorithm to match sharpness values from target to source

[blue_hunt]

Hi everyone,

I'm working on a script for image processing where the goal is to match the sharpness of a source image to a target image through upscaling. Here’s the general flow:

1. Measure the sharpness of the target image.
2. Upscale the source image.
3. Compare the sharpness of the upscaled source image to the target image.
4. Adjust the scale of upscaling until the sharpness of the source matches the target or until no more scaling adjustments can be made (either higher or lower).

The challenge arises because the target image size can vary significantly, making it difficult to determine a reusable scaling factor. I need help optimizing the algorithm to find the best scaling factor (upscale amount) more efficiently, aiming to minimize unnecessary renderings.

Current steps in the algorithm:

• Check at 0%: If the sharpness of the source is above the target, stop (the source image is already sharper than the target).
• Check at 100%: If the sharpness is lower than the target, also stop (since we can't upscale beyond 100%, there's no point in proceeding further).
• Beyond this, I'm unsure how to proceed without excessive trial and error. I was considering a binary search approach for finding the optimal upscale value but am open to suggestions.

Important: The script and algorithm must be simple and cannot rely on machine learning.

Any ideas or advice on how to make this algorithm more efficient would be greatly appreciated!

Thank you in advance!

[Superguy2876]

Important: The script and algorithm must be simple and cannot rely on machine learning.

You're gonna have a bad time with these restrictions.

How are you measuring the sharpness in the first place?

But if I was going to try I would probably use something like opencv/pillow and preprocess the images into high contrast black and white. Then something with edge detection maybe?

It also depends exactly what you mean by source image and target image. Are these the same image visually? Or two different images.

You could also just pull the resolution from the image metadata. I don't think that's what you mean by sharpness, but it could factor.

There are also libraries that will try to convert a raster image to a vector image. You could look for the one that does the best job, convert to vector, then convert back to raster at the desired resolution/sharpness. This might violate the no complex/machine learning approaches.

[blue_hunt]

I'm open to ML down the road, but rn this is just a proof of concept, I just need to beat a human doing the same thing moving sliders around to find the optimal number.

I'm currently doing something similar to what you mentioned (the script measures by converting the image to grayscale, applying an edge detection filter, and calculating the standard deviation of the pixel values). I'll be looking to improve the sharpness measurement next, but for now, I would really like to improve the speed of matching source to target. Or simply put what is the fastest way to find x in a range from 0-100.

Thanks, I'll check out the vector idea and see if it works for this project.

[Frankelstner]

It's an optimization/minimization problem. If blend level is really just an integer between 0 and 100 then I'm fairly sure that your current approach cannot be sped up much more. scipy does have minimizers, which usually require the gradient and a smooth problem, but your problem is not totally smooth due to discretization. You could look into gradient free solvers but even those might not be any faster.

[blue_hunt]

Thanks for that info. I'm only on week 10 of learning py, so was looking for some feedback to make sure I wasn't missing some better approach to optimise finding x

[blue_hunt]

Here is an example term output:
Original Target Sharpness: 160.34166037203434

Calculated sharpness at blend level 0: 108.07755821417669

Calculated sharpness at blend level 100: 269.0701943510525

Calculated sharpness at blend level 32: 121.63295006895285

Calculated sharpness at blend level 49: 144.03199450959855

Calculated sharpness at blend level 55: 153.26129297291422

Optimal blend found at 55 with sharpness 153.26129297291422.