Recovering high dynamic range radiance maps from photographs
Proceedings of the 24th annual conference on Computer graphics and interactive techniques
Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Photographic tone reproduction for digital images
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
What Can Be Known about the Radiometric Response from Images?
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Comparametric equations with practical applications in quantigraphic image processing
IEEE Transactions on Image Processing
Two-layer coding algorithm for high dynamic range images based on luminance compensation
Journal of Visual Communication and Image Representation
High dynamic range global mosaic
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
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Methods for expanding the dynamic range of digital photographs by combining images taken at different exposures have recently received a lot of attention. Current techniques assume that the photometric transfer function of a given camera is the same (modulo an overall exposure change) for all the input images. Unfortunately, this is rarely the case with today's camera, which may perform complex nonlinear color and intensity transforms on each picture. In this paper, we show how the use of probability models for the imaging system and weak prior models for the response functions enable us to estimate a different function for each image using only pixel intensity values. Our approach also allows us to characterize the uncertainty inherent in each pixel measurement. We can therefore produce statistically optimal estimates for the hidden variables in our model representing scene irradiance. We present results using this method to statistically characterize camera imaging functions and construct high-quality high dynamic range (HDR) images using only image pixel information.