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a . Initial HDR Implementation This exercise is the initial implementation of HDR images, as below. You will find on folder a set of photos
a Initial HDR Implementation
This exercise is the initial implementation of HDR images, as below. You will find on folder a set of photos from CR to CR For each of them, the exposure time was half the one of the previous photo.
Combine them to produce HDR raw data. Then, apply the demosaicing algorithm and the white balance. Decrease the dynamic range by computing the logarithm of this data, and downscale it to the interval. Then save the resulting image.
You should obtain the same result as what is shown in the slides.
Simple combination approach:
Load brightest raw data longest exposure
Lets call it h
For each next raw file:
Load it and name it i
Multiply i by the exposure difference to the first photo
Eg first photo s second photo s multiply by
Values in h which are above a threshold t get replaced by the corresponding values in i
t is not that important
Avoid the plateau in i
A value of maxh should be fine
b iCAM
Implement the iCAM HDR method, a iCAM aims at preserving details, without halo effect, and compresses more the base of the image:
Pseudocode:
outputrange
inputintensity red green bluer g b rgb inputintensity
logbase bilatfiltloginputintensity
logdetails loginputintensity logbase
compression logoutputrangemaxlogbaseminlogbaselogoffset maxlogbase compressionoutputintensity explogbase compression logoffset logdetailrgb routputintensity, goutputintensity, boutputintensity. Try different settings to find the ones you like the best.
Hint: bilateral filters are slow, so while developing your solution, use at first very small kernels to minimize the run time.
c Win
or at least try to
Implement a demosaicing function named process raw which takes two parameters:
The path to a raw file CR format
The path to wich the resulting JPG image has to be saved
Hint: you will be judged based on the quality of your
Provide python code without using CV for a b c
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