That's more related to a problem known as infilling. Generally throwing out information is a lot easier than generating it. You can do some statistical things by sampling other points of the image into newly created "gap" but it will probably look bad if fully automated, at least on big changes.
There's an obvious version of the algorithm in that direction. For one line "seam", it's easy enough, you just pull data from either side. But repeatedly applying it the more often your new "seams" end up next to something already estimated, the less real information there is - I suspect this becomes visually noticeable pretty fast.
Are you referring to image inpainting? I think that's what it's usually called (please correct me if I'm wrong though!)
Although I'm not really familiar with traditional algorithms for inpainting, I've seen some ML research do some stuff with it that I found to be really impressive.
The algorithm they describe is able to inpaint pixels AND depth information from existing RGB-D photos, enabling images to be viewed in 3d space and be used with parallax effects. Really cool stuff!
It'd be pretty interesting to train an ML model. You could generate a bunch of training examples: downsize lots of images, then use the upsized versions as targets.
It's not quite the same thing as superresolution, since it's seam carving.
Yeah, I believe Seam Carving paper did this. Pretty simple, find the low-energy path and do linear interpolation between the two neighboring pixels (I think it also introduced some tricks to find n low-energy paths at once to avoid inserting into the same path again and again).
But like the top comment pointed out. This algorithm is easy to implement and interesting, but in real-world examples are not better than salient object detection + cropping.
