As someone who also did some line scan photography, I also ran into a lot of similar challenges!

> Displaying and sharing the pictures I've taken has been a pain throughout the project. Most software on my computer doesn't like how big they are, and the most reliable tool I've found for viewing them has been GNU IMP, which feels a bit overkill.

I vibe-coded my own image viewer, sriv, to address this: https://github.com/dllu/sriv

Also, I found that displaying the images with multiple rows instead of a single super wide image is a lot more ergonomic.

> I correct for it for a given subject by shifting the red and blue channels to line up with the green channel. Since the lines are evenly spaced, I can shift by the same amount in opposite directions rather than having to measure separate offsets for each channel. In theory, I could decide how far to shift by correlating brightness shifts across channels, but at present, I do it manually.

I implemented the correlation-based approach: https://daniel.lawrence.lu/blog/2025-09-21-line-scan-camera-...

Recently, I also vibe-coded a tool for me to manually annotate constraints to fine-tune the distortion correction: https://daniel.lawrence.lu/blog/2026-08-02-line-scan-camera-...

For a stationary camera imaging a train, you just need to estimate the train's speed. But for the inverse problem of imaging the landscape from a moving train, the speed varies due to parallax (near things move faster, far things move slower), so there's no way to fully eliminate the RGB fringing for all distances.

> The biggest one is to make it not dependent on a laptop to capture images, which will make it less sketchy and easier to bring places.

I did just that recently, by adding an NVIDIA Jetson Orin Nano and a 7" 2000 nit touchscreen as well as a custom-designed CNC aluminum case: https://daniel.lawrence.lu/blog/2026-06-12-standalone-cnc-li...

Using the railway network as a flatbed scanner