MULTIMEDIA TECHNOLOGY SMM 3001 MEDIA - IMAGES. Processing digital Images digital images are often processed using “digital filters” digital images are.

Slides:



Advertisements
Similar presentations
1 Sharpening & Smoothing Session 1 AISIG Meeting July 17, 2007 Sam Saeed.
Advertisements

Convolution. Why? Image processing Remove noise from images (e.g. poor transmission (from space), measurement (X-Rays))
Chapter Eleven Digital Darkroom Expert Techniques.
CORRECTING IMAGE COLOR CHAPTER 16. TONAL QUALITY The tonal quality settings in Photoshop enable you to manipulate the image appearance by adjusting highlights.
HOW TO SHARPEN THE IMAGE NATHAN GRAVLEE. DIGITAL MEDIA What is this? – It make an image look for defined and hard-focused. It enhances detail! When do.
Embedded Image Processing on FPGA Brian Kinsella Supervised by Dr Fearghal Morgan.
Digital Image Processing
Grey Level Enhancement Contrast stretching Linear mapping Non-linear mapping Efficient implementation of mapping algorithms Design of classes to support.
Digital Image Processing
Image Processing. Processing Digital Images digital images are often processed using “digital filters” digital filters are based on mathematical functions.
Chapter 4: Image Enhancement
Image Enhancement by Modifying Gray Scale of Individual Pixels
Digital image processing Chapter 6. Image enhancement IMAGE ENHANCEMENT Introduction Image enhancement algorithms & techniques Point-wise operations Contrast.
EEE 498/591- Real-Time DSP1 What is image processing? x(t 1,t 2 ) : ANALOG SIGNAL x : real value (t 1,t 2 ) : pair of real continuous space (time) variables.
6/9/2015Digital Image Processing1. 2 Example Histogram.
CS443: Digital Imaging and Multimedia Point Operations on Digital Images Spring 2008 Ahmed Elgammal Dept. of Computer Science Rutgers University Spring.
Multimedia Data Introduction to Image Processing Dr Mike Spann Electronic, Electrical and Computer.
Processing Digital Images. Filtering Analysis –Recognition Transmission.
1 Vladimir Botchko Lecture 4. Image Enhancement Lappeenranta University of Technology (Finland)
Image Enhancement.
Spectral contrast enhancement
09 March 1999 Digital Image Processing II 1. Single-Band Image Processing Histogram Image contrast enhancement (Linear stretch, histogram equalization)
CS654: Digital Image Analysis Lecture 17: Image Enhancement.
The Digital Image Dr. John Ryan.
Image Enhancement T , Biomedical Image Analysis Seminar presentation Hannu Laaksonen Vibhor Kumar.
Image Processing Image Histogram Lecture
Multimedia Data Introduction to Image Processing Dr Sandra I. Woolley Electronic, Electrical.
EE663 Image Processing Dr. Samir H. Abdul-Jauwad Electrical Engineering Department King Fahd University of Petroleum & Minerals.
AdeptSight Image Processing Tools Lee Haney January 21, 2010.
Digital Image Processing Part 1 Introduction. The eye.
Image Processing Part II. 2 Classes of Digital Filters global filters transform each pixel uniformly according to the function regardless of its location.
Machine Vision ENT 273 Image Filters Hema C.R. Lecture 5.
DIGITAL IMAGE. Basic Image Concepts An image is a spatial representation of an object An image can be thought of as a function with resulting values of.
Image Manipulation CSC361/661 – Digital Media Spring 2002 Burg/Wong.
Welcome eager young artists! Ms. Edelman Monday, December 14, 2015  DO NOW: take out your notebook and a pen. Review your notes from yesterday.
Intelligent Vision Systems ENT 496 Image Filtering and Enhancement Hema C.R. Lecture 4.
Digital Image Processing EEE415 Lecture 3
Lecture # 19 Image Processing II. 2 Classes of Digital Filters Global filters transform each pixel uniformly according to the function regardless of.
Lightness filtering in color images with respect to the gamut School of Electrical Engineering and Computer Science Kyungpook National Univ. Fourteenth.
Digital Image Processing
Digital Image Processing Part 3 Spatial Domain Processing.
EE 7730 Image Enhancement. Bahadir K. Gunturk2 Image Enhancement The objective of image enhancement is to process an image so that the result is more.
Instructor: Mircea Nicolescu Lecture 5 CS 485 / 685 Computer Vision.
Introduction to Digital Image Analysis Kurt Thorn NIC.
Chapter Ten Essential Image Enhancement. Evaluate the Image Use full screen (press ‘F’) in Photoshop to view the whole image Check exposure (histogram.
Image Enhancement Band Ratio Linear Contrast Enhancement
IMAGE PROCESSING Tadas Rimavičius.
© 2016 Pearson Education, Inc., Hoboken, NJ. All rights reserved.
(Project) by:- ROHAN HIMANSHU ANUP 70282
Image enhancement algorithms & techniques Point-wise operations
"Digital Media Primer" Yue-Ling Wong, Copyright (c)2013 by Pearson Education, Inc. All rights reserved.
IMAGE PROCESSING INTENSITY TRANSFORMATION AND SPATIAL FILTERING
Image Enhancement.
CSC020, Computer Graphics Adjustment Layers 1.
Discussion #29 – Images II
Image Processing - in short
Digital Image Processing
Histogram Histogram is a graph that shows frequency of anything. Histograms usually have bars that represent frequency of occuring of data. Histogram has.
Chapter 8, Exploring the Digital Domain
Image Enhancement in the Spatial Domain
Lecture 3 (2.5.07) Image Enhancement in Spatial Domain
CSC 381/481 Quarter: Fall 03/04 Daniela Stan Raicu
Resolution Resolution: 6 x 4.
Digital Image Processing
Grey Level Enhancement

Topic 1 Three related sub-fields Image processing Computer vision
Intensity Transform Contrast Stretching Y ← u0+γ*(Y-u)/s
Histogram The histogram of an image is a plot of the gray _levels values versus the number of pixels at that value. A histogram appears as a graph with.
Presentation transcript:

MULTIMEDIA TECHNOLOGY SMM 3001 MEDIA - IMAGES

Processing digital Images digital images are often processed using “digital filters” digital images are often processed using “digital filters” digital filters are based on mathematical functions that operate on the pixels of the image digital filters are based on mathematical functions that operate on the pixels of the image

Processing digital Images there are two classes of digital filters: global and local there are two classes of digital filters: global and local global filters transform each pixel uniformly according to the function regardless of its location in the image global filters transform each pixel uniformly according to the function regardless of its location in the image Eg. Adjusting the overall tonal qualities, lightness & darkness Eg. Adjusting the overall tonal qualities, lightness & darkness local filters transform a pixel depending upon its relation to surrounding ones local filters transform a pixel depending upon its relation to surrounding ones Eg. Sharpening the edges Eg. Sharpening the edges

Global Filters Brightness and Contrast control Brightness and Contrast control Brightness refers to the overall intensity of the pixels in the image Brightness refers to the overall intensity of the pixels in the image Image can be improved by lightening/darkening Image can be improved by lightening/darkening Grayscale or black & white image – by adding intensity values from each pixel Grayscale or black & white image – by adding intensity values from each pixel Contrast – relative difference between distributions of lighter & darker pixels in an image Contrast – relative difference between distributions of lighter & darker pixels in an image Histogram thesholding Histogram thesholding Histogram stretching or equalization Histogram stretching or equalization Color corrections Color corrections Hue-shifting and colorizing Hue-shifting and colorizing Inversions Inversions

Global Filters a histogram is a graph depicting the frequency distribution of pixel values in the image a histogram is a graph depicting the frequency distribution of pixel values in the image thresholding creates a binary image by converting pixels according to a threshold value thresholding creates a binary image by converting pixels according to a threshold value The threshold operation chooses an intensity value to serve as cutoff point for redistributing the pixel values in the image The threshold operation chooses an intensity value to serve as cutoff point for redistributing the pixel values in the image

Global Filters

Histogram stretching redistributes pixel values in the image that has poor contrast Histogram stretching redistributes pixel values in the image that has poor contrast Sometimes the pixel values may be clustered toward the middle of the range Sometimes the pixel values may be clustered toward the middle of the range Equalization improves images with poor contrast Equalization improves images with poor contrast

Global Filters Histogram stretching redistributes the intensity of colour values of the pixels in Image. The darkest pixels in the image are mapped to some minimum value (Min) While the lightest pixels are made the maximum brightest value (Max).

Global Filters Hue-shifting is used to modify the color makeup of an image Hue-shifting is used to modify the color makeup of an image Usually this is done only for selected areas for colour corrections, balancing, or special effects Usually this is done only for selected areas for colour corrections, balancing, or special effects Pseudo-coloring assigns hues to intensity ranges for better rendering of details Pseudo-coloring assigns hues to intensity ranges for better rendering of details Colorized image of Mississippi at Vicksburg

Local Filters The filter/mask serves as a template defining how the pixel will be transformed in relation to its neighbours The filter/mask serves as a template defining how the pixel will be transformed in relation to its neighbours Sharpening Sharpening Blurring Blurring Unsharp Masking Unsharp Masking Edge and line detection Edge and line detection Noise filters Noise filters

Local Filters Edge and line detection filters subtract all parts of the image except edges or boundaries between two different regions Edge and line detection filters subtract all parts of the image except edges or boundaries between two different regions edge detection is often used to recognized objects of interest in the image edge detection is often used to recognized objects of interest in the image

Local Filters edges and lines detected in an image of toy blocks