This is a python project from recovering the response curve to reconstruct the irradiance map(HDR) in the real scene.

• Project Description
• Algorithms
• Usages
• Results

There are several steps(shown below) between the scene radiance and the image we see. Starting from taking few photographs in different exposures, we use Paul Debevec's method to recover the response curve by these photographs. After we got the response curve, we are now able to reconstuct the irradiance map also known as HDR images. Also, we developed a global tone mapping algorithm to see the combination of these photographs.

The function of response curve is a general term for the many steps shown above. To recover from pixel values record on image to radiance map we need a function g which is: .

• is the unknown response function

• is a linear weighting function. g will be less smooth and will fit the data more poorly near extremes (Z=0 or Z=255). Debevec introduces a weighting function to enphasize the smoothness fitting terms toward the middle of the curve.

• is the exposure time in index

• is the unknown radiance in different pixel

• : is the observed value in pixel and exposure time

• is the pixel location index, is the exposure index and P is the total number of exposures.

And now we can reconstruct the irradiance map by the formula below. After this step, we are able to have the output.hdr file.

In this part, we implemented a naive global tone mapping algorithm(shown below) to get a image which can match our visual experience. and now we can have the result.

Exposure 1/160 sec	Exposure 1/125 sec	Exposure 1/80 sec	Exposure 1/60 sec	Exposure 1/40 sec	Exposure 1/15 sec

Exposure 1/400 sec	Exposure 1/250 sec	Exposure 1/100 sec	Exposure 1/40 sec	Exposure 1/25 sec	Exposure 1/8 sec	Exposure 1/3 sec

• Digital Visual Effects
• high-dynamic-range-image
• HDR-Imaging