Transfer learning on images with higher dynamic range Is it possible to fine-tune a CNN-based model previously trained on grey-scale images with 8 bits depth  [0 ~ 2^8] to fit a 16 bits depth [0 ~ 2^16] images?
if there is any research paper that confirm that, it would be great if you share it

In most applications, CNNs treat pixel values as a continuous variable ranging (without loss of generality) from 0 to 1. There's no substantial difference between mapping 256 discrete values onto a continuous range, versus mapping 65536 values onto this continuous range.
NB - "high dynamic range" is an very overloaded term. Literally, it means "using more bits per pixel in order to store a wider range of brightness values". Many standards of storing "HDR" info do so in linear space, rather than in some "perceptually uniform space". So, it's easy (and common) to conflate "HDR" with "16/32-bit linear color space". I think it would be a mistake to use a CNN on a linear values rather than perceptually uniform ones.
