There are a lot of steps like that provided you want an image that you want to show to the user (i.e. Jpeg). You do have somehow merge the 3 Bayer filter detections on rectangular grid, which involves interpolation. You do have to subtract some sort of bias in a detector, possibly correct for different sensitivity across the detector. You have to map the raw 'electron counts' into Jpeg scale which involves another set of decisions/image processing steps

There is clear processing in terms of interpreting the raw sensor data as you're describing. Then there are blurrier processes still, like "denoising" and "upscaling", which straddle the line between bias-correction and alteration. Then there's modification of actual color and luminance as the parent was describing. Now we're seeing full alterations applied automatically with neural nets, literally altering shapes and shadows and natural lighting phenomena.

I think it's useful to distinguish all of these even if they are desired. I really love my iPhone camera, but there's something deeply unsettling about how it alters the photos. It's fundamentally producing a different image you can get with either film or through your eyes. Naturally this is true for all digital sensors but we once could point out specifically how and why the resulting image differs from what our eyes see. It's no longer easy to even enumerate the possible alterations that go on via software, let alone control many of them, and I think there will be backlash at some point (or stated differently, a market for cameras that allow controlling this).

I've got to imagine it's frustrating for people who rely on their phone cameras for daily work to find out that upgrading a phone necessarily means relearning its foibles and adjusting how you shoot to accommodate it. Granted, I mostly take smartphone photos in situations where i'd rather not be neurotic about the result (candids, memories, reminders, etc) but surely there are professionals out there who can speak to this.