I have a video, whose frames I have extracted and require to work with. 16 bits and grayscale images. My task is to improve the quality of the images, by removing the noise + adding tone mapping and gamma. I want do this because I want to compare how it holds up against a specilized technique I found to do the same. I basically want to know if this specilized technique has something unique to offer, or if I can get the same output by just denoising and doing some image corrections.
I am fairly new to programming and denoising images, so I am not sure what the best technique to do this is. I have listed what I'd like to do and how I am approaching it below, please let me know if there is a better way.
Measure the noise level across a given frame in the image: I want to quantify how much noise exists in the image before/after my corrections are applied and compare it to the noise levels from the technique. The frames have a lot of detail in them so is noise measurement across the whole frame even possible? Or will I have to comapre wrt a flatfield within the image? How do I know the noise level at a different part of the image isn't different?
Remove row noise and column noise, both temporal and fixed from the image. If I take a row value and the frame value, and correct each pixel so that the row vlaue = frame value, will I be correcting for temporal noise? I have multiple frames because they were extracted from a video, but the contents of the image are not the same. Could I find a flatfield across all images and find the average pixel value in that region, and then apply that average across the image? I do not have the dark frames of the images (only the images themselves) so how can I do a dark frame subtraction for removing fixed pattern noise?
Apply a gamma curve to do some tone mapping