Denoising the high dynamic range imaging process: A comparative study using SNR and detail loss criteria

High dynamic range (HDR) imaging techniques have been applied more and more in recent years. They are mostly based on the basic principle of acquiring differently exposed images of the scene, linearizing each image, and finally combining the resulting exposure set into one HDR image using some weighting scheme. This process is affected by different noise sources during image acquisition. In this paper we comprehensively study the noise reduction properties of HDR imaging. We show that the fact that individual images partially exhibit the same structures with independent noise can be utilized to further improve the signal-to-noise ratio (SNR). To this end, we model the whole imaging process and evaluate resulting HDR images using artificially created as well as real-world scenes. We compare imaging integrated denoising to denoising of the LDR input data as well as denoising the resulting HDR images directly in terms of SNR gain and detail loss.