Wavelet based denoising by correlation analysis for high dynamic range imaging

High dynamic range (HDR) imaging is used to acquire the full dynamic range of a scene with a camera of limited dynamic range. To this end, an exposure set of the scene is acquired, followed by the linearization of each image with the inverse camera transfer function (CTF), which needs to be measured or estimated. Subsequently, the images are combined into one HDR image. Several weighting functions have been proposed for this combination. Naturally, each individual image is afflicted with noise from the acquisition process. The resulting HDR image features a higher SNR than the acquired images as a consequence of the weighted averaging during reconstruction. We show that the fact that the individual images partially show the same structures with independent noise can be utilized to further improve the SNR. Thus, we propose a wavelet-based denoising using correlation analysis between different images from the exposure set that outperforms the denoising properties of commonly applied weighted averages.