Lecture Digital image processing: Image arithmetic - Nguyễn Công Phương

Lecture Digital image processing - Image arithmetic include all of the following: Operator basics, image treatment (addition, subtraction, multiplication, division, blending). Inviting you refer.
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Lecture Digital image processing: Image arithmetic - Nguyễn Công Phương Nguyễn Công Phương DIGITAL IMAGE PROCESSING Image Arithmetic Contents I. Introduction to Image Processing & Matlab II. Image Acquisition, Types, & File I/O III. Image Arithmetic IV. Affine & Logical Operations, Distortions, & Noise in Images V. Image Transform VI. Spatial & Frequency Domain Filter Design VII. Image Restoration & Blind Deconvolution VIII. Image Compression IX. Edge Detection X. Binary Image Processing XI. Image Encryption & Watermarking XII. Image Classification & Segmentation XIII. Image – Based Object Tracking XIV. Face Recognition XV. Soft Computing in Image Processing sites.google.com/site/ncpdhbkhn 2 Image Arithmetic 1. Operator Basics 2. Image Treatment sites.google.com/site/ncpdhbkhn 3 Operator Basics a b c  d e f g h i a b c j k l d e f sites.google.com/site/ncpdhbkhn 4 Image Arithmetic 1. Operator Basics 2. Image Treatment 1. Addition 2. Subtraction 3. Multiplication 4. Division 5. Blending sites.google.com/site/ncpdhbkhn 5 Pixel Addition (1) a b c  d e f a+g b+h c+i d+j e+k f+l g h i j k l R ( m, n )  P ( m, n )  Q ( m, n ) sites.google.com/site/ncpdhbkhn 6 Pixel Addition (2) C  a b c d e f R ( m, n )  P ( m, n )  C sites.google.com/site/ncpdhbkhn 7 Image Arithmetic 1. Operator Basics 2. Image Treatment 1. Addition 2. Subtraction 3. Multiplication 4. Division 5. Blending sites.google.com/site/ncpdhbkhn 8 Pixel Subtraction R ( m , n )  P ( m, n )  Q ( m , n ) R ( m, n )  P ( m, n )  Q ( m, n ) R ( m, n )  P ( m, n )  C sites.google.com/site/ncpdhbkhn 9 Pixel Multiplication & Scaling R ( m, n )  P ( m, n )  Q ( m, n ) R ( m, n )  P ( m , n )  C sites.google.com/site/ncpdhbkhn 10 Dividing Images R ( m , n )  P ( m, n )  Q ( m , n ) R ( m, n )  P ( m, n )  C sites.google.com/site/ncpdhbkhn 11 Image Blending & Linear Combinations R( m, n )  k1P( m, n )  k2Q ( m, n ) sites.google.com/site/ncpdhbkhn 12