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High Dynamic Range Image Reconstruction from Hand-held Cameras

High Dynamic Range Image Reconstruction from Hand-held Cameras. Pei-Ying Lu Tz-Huan Huang Meng -Sung Wu Yi-Ting Cheng Yung-Yu Chuang National Taiwan University CVPR ’09 Reporter : A nnie Lin. Outline . Introduction Algorithm Results and comparison . Introduction .

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High Dynamic Range Image Reconstruction from Hand-held Cameras

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  1. High Dynamic Range Image Reconstruction from Hand-held Cameras Pei-Ying Lu Tz-Huan Huang Meng-Sung Wu Yi-Ting Cheng Yung-Yu Chuang National Taiwan University CVPR ’09 Reporter : Annie Lin

  2. Outline • Introduction • Algorithm • Results and comparison

  3. Introduction • Reconstructing a high-quality high dynamic range (HDR) image from a set of differently exposed and possibly blurred images taken with a hand-held camera • Input : a series captured images • Bayesian framework to formulate the problem and apply a maximum likelihood approach to iteratively perform blur kernel estimation ,HDR reconstruction and camera curve recovery • Goal: to find the motion blur kernel, irradianceand response function

  4. Related work • Camera pipeline • HDR • Image deblurring • Assuming the blur kernel is shift-invariant

  5. Algorithm • Image alignment • The Bayesian framework • Optimization • Tikhonov regularization • H. W. Engl, M. Hanke, and A. Neubauer. Regularization of Inverse Problems. Kluwer Academic, 2000. (book) • Three parameters: • K (motion blur kernel) • E (irradiance ) • g ( response function)

  6. The Bayesian framework • Z: captured image • f: camera response function (unknown) • E: irradiance (unknown) • K: motion blur kernel (unknown) • Overdetermined • the problem is then turned into "a maximum likelihood” problem

  7. Optimization • Initialization • Ki = δ • E [2]Recovering high dynamic range radiance maps from photographs. • G a linear mapping from the pixel values to the irradiance values • Tikhonov regularization (ill-posed) • Optimizing Ki • Optimizing E • Optimizing g • To iteratively update the data : • [2] Landweber method “H. W. Engl Regularization of Inverse Problems”

  8. Optimizing Ki • The iteration stops when the change between two steps is sufficiently small

  9. Optimizing E • After optimization, scaling up the reconstructed irradiance E to keep its values in a similar scale as the initialized irradiance • scaling factor :the ratio of mean values, mean(E initial)=mean(E). • Optimizing g • Add smoothness turn to ensure the function g is smooth • λ3 weighted:

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