Image selective smoothing and edge detection by nonlinear diffusion
SIAM Journal on Numerical Analysis
Recovering high dynamic range radiance maps from photographs
Proceedings of the 24th annual conference on Computer graphics and interactive techniques
Bilateral Filtering for Gray and Color Images
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
SSIAI '06 Proceedings of the 2006 IEEE Southwest Symposium on Image Analysis and Interpretation
RGB calibration for color image analysis in machine vision
IEEE Transactions on Image Processing
Comparametric equations with practical applications in quantigraphic image processing
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Minimal-Bracketing Sets for High-Dynamic-Range Image Capture
IEEE Transactions on Image Processing
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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.