S. Mandayam/ DIP/ECE Dept./Rowan University Digital Image Processing ECE /ECE Fall 2007 Shreekanth Mandayam ECE Department Rowan University Lecture 3 September 24, 2007

S. Mandayam/ DIP/ECE Dept./Rowan UniversityPlan Pixel Operations Point processing Histogram equalization Connectivity Image Enhancement Spatial Filtering Detection of Discontinuities Edge detection (Sobel, Prewitt and Laplacian masks) individual pixels all pixels neighboring pixels Low-pass High-pass

S. Mandayam/ DIP/ECE Dept./Rowan University DIP: Details

S. Mandayam/ DIP/ECE Dept./Rowan University Image Preprocessing Enhancement Restoration Spatial Domain Spectral Domain Point Processing >>imadjust >>histeq Spatial filtering >>filter2 Filtering >>fft2/ifft2 >>fftshift Inverse filtering Wiener filtering

S. Mandayam/ DIP/ECE Dept./Rowan University Pixel Connectivity (x+1,y-1) (x,y) (x+1,y) (x+1,y+1) (x,y+1) (x-1,y+1) (x-1,y-1) (x,y-1) (x-1,y) x y

S. Mandayam/ DIP/ECE Dept./Rowan University Labeling of Connected Components Begin scan Position: (x,y) p(x,y) = 1? p(x-1,y) = 1? p(x,y-1) = 1? p(x-1,y) AND p(x,y-1) = 1 class(x,y) = new class Update position (x,y) class(x,y) = class(x-1,y) class(x,y) = class(x,y-1) class(x-1,y) = class(x,y-1) All positions scanned? End scan y y y y y p(x-1,y) AND p(x,y-1) = 0 y

S. Mandayam/ DIP/ECE Dept./Rowan University Spatial Filtering (Masking) R w1w1 w2w2 w3w3 w4w4 w5w5 w6w6 w7w7 w8w8 w9w9 z1z1 z2z2 z3z3 z4z4 z5z5 z6z6 z7z7 z8z8 z9z9 Portion of a digital image Mask = w 1 z 1 + w 2 z 2 + ….. +w 9 z 9 Replace with

S. Mandayam/ DIP/ECE Dept./Rowan University Low-pass Filters R z1z1 z2z2 z3z3 z4z4 z5z5 z6z6 z7z7 z8z8 z9z9 Moving Average Filter = median(z 1, z 2, ….., z 9 ) Replace with (1/9)* Median Filter

S. Mandayam/ DIP/ECE Dept./Rowan University Noise Models SNR g = 10 log 10 (P f /P n ) Power Variance (how?) SNR g = 10 log 10 (  f 2 /  n 2 ) f(x,y) g(x,y) n(x,y)  Degradation Model: g = f + n

S. Mandayam/ DIP/ECE Dept./Rowan University Noise Models N(0,1): zero-mean, unit-variance, Gaussian RV Theorem: N(0,  2 ) =  N(0,1) Use this for generating normally distributed r.v.’s of any variance >>imnoise >>nrfiltdemo >>filter2 demos/demo2spatial_filtering/lowpassdemo.m

S. Mandayam/ DIP/ECE Dept./Rowan University High-pass Filters 8 z1z1 z2z2 z3z3 z4z4 z5z5 z6z6 z7z7 z8z8 z9z9 Basic HP Filter (1/9)* Gradient Filter demos/demo2spatial_filtering/highpassdemo.m

S. Mandayam/ DIP/ECE Dept./Rowan University Detection of Discontinuities Point Detection Line Detection (Prewitt’s Gradient) demos/demo2spatial _filtering/prewitt.m

S. Mandayam/ DIP/ECE Dept./Rowan University Edge Detection Sobel Masks >>edgedemo >>edge demos/demo2spatial_filtering/edgegradientdemo.m

S. Mandayam/ DIP/ECE Dept./Rowan University Lab 1: Pixel Operations

S. Mandayam/ DIP/ECE Dept./Rowan UniversitySummary