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Lecture Digital image processing - Lecture 19: Image restoration

This chapter presents the following content: Image restoration techniques, difference between image enchantment and image restoration, image formation process and the degradation model, degradation model in continues function and its discrete formulation, discrete formulation for 1D and 2D. | Digital Image Processing CSC331 Image restoration 1 Summery of previous lecture Image Enhancement techniques Spatial domain techniques Pont processing techniques Mask Processing techniques Frequency domain techniques Ideal and Butterworth lowpass filter Ideal and Butterworth highpass filer Gaussian filter Homemorphic filter 2 Todays lecture Image restoration techniques Difference between image enchantment and image restoration Image formation process and the degradation model Degradation model in continues function and its discrete formulation Discrete formulation for 1D and 2D 3 Objectives of Image Restoration 4 Overview of restoration techniques 5 Degradation model 6 Definitions 7 Defining linearity (Super position theorem) 8 Homogeneity property 9 Degradation model in case of continuous functions 10 11 Impulse response (point spread function) 12 H as position invariant 13 Discrete formulation degradation model First in case of 1 dimension and later it will be extend to 2 dimensional cases for digital image processing operations. For simplicity, initially, we will neglect the contribution of the noise term that is eta (x, y). 14 15 16 17 2D Discrete formulation degradation model 18 19 20 Summery of the lecture Image restoration techniques Difference between image enchantment and image restoration Image formation process and the degradation model Degradation model in continues function and its discrete formulation Discrete formulation for 1D and 2D 21 References Prof .P. K. Biswas Department of Electronics and Electrical Communication Engineering Indian Institute of Technology, Kharagpur Gonzalez R. C. & Woods R.E. (2008). Digital Image Processing. Prentice Hall. Forsyth, D. A. & Ponce, J. (2011).Computer Vision: A Modern Approach. Pearson Education. . | Digital Image Processing CSC331 Image restoration 1 Summery of previous lecture Image Enhancement techniques Spatial domain techniques Pont processing techniques Mask Processing techniques Frequency domain techniques Ideal and Butterworth lowpass filter Ideal and Butterworth highpass filer Gaussian filter Homemorphic filter 2 Todays lecture Image restoration techniques Difference between image enchantment and image restoration Image formation process and the degradation model Degradation model in continues function and its discrete formulation Discrete formulation for 1D and 2D 3 Objectives of Image Restoration 4 Overview of restoration techniques 5 Degradation model 6 Definitions 7 Defining linearity (Super position theorem) 8 Homogeneity property 9 Degradation model in case of continuous functions 10 11 Impulse response (point spread function) 12 H as position invariant 13 Discrete formulation degradation model First in case of 1 dimension and later it will be extend to 2 dimensional cases for digital image processing operations. For simplicity, initially, we will neglect the contribution of the noise term that is eta (x, y). 14 15 16 17 2D Discrete formulation degradation model 18 19 20 Summery of the lecture Image restoration techniques Difference between image enchantment and image restoration Image formation process and the degradation model Degradation model in continues function and its discrete formulation Discrete formulation for 1D and 2D 21 References Prof .P. K. Biswas Department of Electronics and Electrical Communication Engineering Indian Institute of Technology, Kharagpur Gonzalez R. C. & Woods R.E. (2008). Digital Image Processing. Prentice Hall. Forsyth, D. A. & Ponce, J. (2011).Computer Vision: A Modern Approach. Pearson Education. 22