Digital image formation.ppt - Mathematical Imaging Lecture 1 Introduction to mathematical image processing What is our goal in Math Mathematical Image

# Digital image formation.ppt - Mathematical Imaging Lecture...

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Mathematical Imaging Lecture 1: Introduction to mathematical image processing

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What is our goal in Math? Mathematical + Image Processing IMAGE PROCESSING TASKS: Denoising, Segmentation, Registration, Compression,… MATHEMATICS: Linear algebra, Calculus, transformation,…
What is our goal in Math 3360? Topic to be covered: Introduction to digital images and imaging problems: Digital images, point spread functions, image transformation, separable, shift-invariant etc. Image compression by image decomposition: SVD, Haar transform, Walsh tranform, DFT, DCT, EDCT, ODCT, FFT Image denoising: What’s noise? Denoising in the spatial domain: Linear/non-linear filtering, anisotropic diffusion, energy minimization method (famous ROF model); Denoising in the frequency domain: High-pass/low-pass filtering Image deblurring: Inverse filtering, Weiner filter, Constrained least-square filtering Image segmentation: Active contour; Famous Chan-Vese segmentation model Image contrast enhancement: Histogram equalization, histogram matching

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Some tastes about IMAGING Image denoising: Image can be corrupted by “noises” during transmission or error during capturing the image intensity Reconstruct a “clean” (usually visually) image from the noisy one
Some tastes about IMAGING Image denoising: Where is the MATHEMATICS ? Minimization model: Solving PDE: Don’t worry about the mathematics! You will learn it (simple version) and find it easy later!

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Some tastes about IMAGING Image segmentation: Image may contain too much information.
• Fall '17
• Dr.hatem

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