是否可以使用 Python 将低质量图像转换为高质量图像？

Question

I made a tif image based on a 3d model of a woodsheet. (x, y, z) represents a point in a 3d space. I simply map (x, y) to a pixel position in the image and (z) to the greyscale value of that pixel. It worked as I have imagined. Then I ran into a low-resolution problem when I tried to print it. The tif image would get pixilated badly as soon as it zooms out. My research suggests that I need to increase the resolution of the image. So I tried a few super-resolution algos found from online sources, including this one https://learnopencv.com/super-resolution-in-opencv/ The final image did get a lot bigger in resolution (10+ times larger in either dimension) but the same problem persists - it gets pixilated as soon as it zooms out, just about the same as the original image.

Looks like quality of an image has something to do not only with resolution of it but also something else. When I say quality of image, I mean how clear the wood texture is in the image. And when I enlarge it, how sharp/clear the texture remains in the image. Can anyone shed some light on this? Thank you.

original tif

The algo generated tif is too large to be included here (32M)

Gigapixel enhanced tif

Update - Here is a recently achieved result: with a GAN-based solution It has restored/invented some of the wood grain details. But the models need to be retrained.

Answer 1

In short, it is possible to do this via deep learning reconstruction like the Super Resolution package you referred to, but you should understand what something like this is trying to do and whether it is fit for purpose.

Generic algorithms like the Super Resolution is trained on variety of images to "guess" at details that is not present in the original image, typically using generative training methods like using the low vs high resolution version of the same image as training data.

Using a contrived example, let's say you are trying to up-res a picture of someone's face (CSI Zoom-and-Enhance style!). From the algorithm's perspective, if a black circle is always present inside a white blob of a certain shape (ie a pupil in an eye), then next time it the algorithm sees the same shape it will guess that there should be a black circle and fill in a black pupil. However, this does not mean that there is details in the original photo that suggests a black pupil.

In your case, you are trying to do a very specific type of up-resing, and algorithms trained on generic data will probably not be good for this type of work. It will be trying to "guess" what detail should be entered, but based on a very generic and diverse set of source data.

If this is a long-term project, you should look to train your algorithm on your specific use-case, which will definitely yield much better results. Otherwise, simple algorithms like smoothing will help make your image less "blocky", but it will not be able to "guess" details that aren't present.