GoKawiilConventional upscaling methods work on the basic principle of adding new pixels that are an average of those surrounding them. The difference is how many pixels are being factored into that average and the weight assigned to each. For this reason, traditional upscaling won't magically make your photos more detailed.
Nearest-neighbor upscaling is an exception to the pixel-averaging principle because it simply copies the nearest pixel wholesale. This makes it perfect for pixel art, diagrams and solid geometric shapes, as it retains their blocky appearance. But on regular photos and anything with a gradient, nearest-neighbor introduces aliasing, which makes the edges look pixelated.
A step up from nearest-neighbor is bilinear upscaling, which creates a new pixel by taking an average of its four surrounding pixels. This method doesn't produce a blocky look but can often make images overly soft, as if they're slightly out of focus. Use it for images that aren't sharp to begin with. Avoid it for upscaling close-up shots and anything else with fine details.
Bicubic upscaling strikes a good balance between resource usage and image quality. Instead of four pixels, it considers the surrounding 16 pixels, with more weight given to the closest ones. Thanks to this, it produces sharper images than bilinear upscaling, all without the jagged edges seen in nearest-neighbor upscaling. It's a safe bet for most types of photos, but know that it can introduce halos unlike the previous two methods.
There are other more sophisticated upscaling algorithms such as the Lanczos method. But these take up more processing power, and I didn't find the results that much better than bicubic upscaling. Try Lanczos only if you're dissatisfied with bicubic upscaling.