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Estimation Camera Response Functions using Probabilistic Intensity Similarity

Estimation Camera Response Functions using Probabilistic Intensity Similarity. CVPR 2008 Advisor : Prof. Huei-Yung Lin Reporter : 品芝. Outline.

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Estimation Camera Response Functions using Probabilistic Intensity Similarity

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  1. Estimation Camera Response Functions using Probabilistic Intensity Similarity CVPR 2008 Advisor : Prof. Huei-Yung Lin Reporter : 品芝

  2. Outline • Past: assume that the image intensity is linearlyrelated to scene radiance, but this assumption does not hold with most camera-non-linearcamera response functions • Introduction • Camera response function • Noise- based estimation • Probabilistic intensity similarity measure • Method • Experiment • Conclusion

  3. Introduction • The image exhibit slight difference in intensity because of noise. Using the intensity fluctuations to estimate the response functionby maximizing the intensity similarityfunction for all pixels. • Noise- based estimation • Noise causes pixel values at the same pixel coordinate to vary in these images, even though they measure the same scene radiance • Probabilistic intensity similarity • Represents the likelihood of two intensity observations corresponding to the same scene radiance in the presence of noise

  4. Camera Response function • The relationship between the measured image intensity Oand irradiance at a cameraI • inverse response function • (1). the similarity varies with the underlying noise distributions and the shape of the response functions • (2). Estimate the response function by maximizing the similarity among the input images

  5. Method • The intensity similarity varies with the shape of the response function • The intensity similarity between two measure intensities is defined • For all outputs

  6. Image similarity and estimation method • Energy function for estimating maximize the image similarity • Image similarity • Physics-based noise model • Energy function for estimating the response function • Maximize the image similarity

  7. Experiment

  8. Conclusion • A method for estimating camera response functions by maximizing the image similarity measure defined as the integral of the probabilistic intensity similarity • Estimate inverse response functions from only a few images

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