Lecture - Image Restoration

Lecture - Image Restoration - ImageAnalysis Image...

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Image Analysis Image Restoration
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Bahadir K. Gunturk EE4780 2 Image Restoration ( , ) ( , )* ( , ) ( , ) g x y h x y f x y n x y = + Image enhancement tries to improve subjective image quality. Image restoration tries to recover the original image.
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Bahadir K. Gunturk EE4780 3 Noise Models ( 29 2 2 2 1 ( ) 2 z p z e μ σ πσ - - = Gaussian noise • Noise may arise during image due to sensors, digitization, transmission, etc. • Most of the time, it is assumed that noise is independent of spatial coordinates, and that there is no correlation between noise component and pixel value. • Noise may be considered as a random variable, its statistical behavior is characterized by a probability density function (PDF).
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Bahadir K. Gunturk EE4780 4 Noise Models Uniform noise Impulse (salt-and-pepper) noise
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Bahadir K. Gunturk EE4780 5 Noise Models Original Noisy images and their histograms
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Bahadir K. Gunturk EE4780 6 Noise Models How to estimate noise parameters? • If imaging device is available • Take a picture of a flat surface. • See the shape of the histogram; decide on the noise model. • Estimate the parameters. (e.g., find mean and standard deviation.) • When only images already generated are available • Get a small patch of image with constant gray level • Inspect histogram • Estimate the parameters
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Bahadir K. Gunturk EE4780 7 Restoration When There is Only Noise Low-Pass Filters: Smoothes local variations in an image. Noise is reduced as a result of blurring.
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This note was uploaded on 11/28/2011 for the course EE 4780 taught by Professor Staff during the Spring '08 term at LSU.

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Lecture - Image Restoration - ImageAnalysis Image...

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