Resolution Resolution: 6 x 4.

Slides:



Advertisements
Similar presentations
CS Spring 2009 CS 414 – Multimedia Systems Design Lecture 4 – Digital Image Representation Klara Nahrstedt Spring 2009.
Advertisements

Chapter Eleven Digital Darkroom Expert Techniques.
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.
Computational Biology, Part 23 Biological Imaging II Robert F. Murphy Copyright  1996, 1999, All rights reserved.
© by Yu Hen Hu 1 ECE533 Digital Image Processing Image Enhancement in Frequency Domain.
Image enhancement in the spatial domain. Human vision for dummies Anatomy and physiology Wavelength Wavelength sensitivity.
GIMP Tutorial v2.0 Boo Virk
The “Orton” Effect. Here’s the starting image. First step is to duplicate the layer, just drag the ‘background’ layer to the new layer icon.
Lecture 2: Filtering CS4670/5670: Computer Vision Kavita Bala.
Graphics&Design Correction and Adjustment - Creative: colour & tone, dust & marks, sharp Tech: Gamma, Histograms, & Convolution.
Multimedia Data Introduction to Image Processing Dr Mike Spann Electronic, Electrical and Computer.
Introduction to Digital Image Processing and Digital Image Analysis.
MSU CSE 803 Stockman Linear Operations Using Masks Masks are patterns used to define the weights used in averaging the neighbors of a pixel to compute.
1 Vladimir Botchko Lecture 4. Image Enhancement Lappeenranta University of Technology (Finland)
Computer Vision Introduction to Image formats, reading and writing images, and image environments Image filtering.
Computer Vision Lecture 3: Digital Images
MSU CSE 803 Linear Operations Using Masks Masks are patterns used to define the weights used in averaging the neighbors of a pixel to compute some result.
Chapter 6 Color Image Processing Chapter 6 Color Image Processing.
Multimedia Systems & Interfaces Karrie G. Karahalios Spring 2007.
GIMP Graphic Image Manipulation Program. GIMP Image manipulation software Free Open Source Written by two students First version in 1996.
Machine Vision ENT 273 Image Filters Hema C.R. Lecture 5.
CS559: Computer Graphics Lecture 3: Digital Image Representation Li Zhang Spring 2008.
Multimedia Data Introduction to Image Processing Dr Sandra I. Woolley Electronic, Electrical.
Aim: How can we use Photoshop to sharpen edges in an image? Do Now: Open the image you worked with yesterday.
Digital Media Dr. Jim Rowan ITEC 2110 Bitmapped Images.
MULTIMEDIA TECHNOLOGY SMM 3001 MEDIA - IMAGES. Processing digital Images digital images are often processed using “digital filters” digital images are.
ANM 100 ADOBE CREATIVE SUITE LESSON 7 1) No Quiz 2) Digital Image Composites in AdobePhotoshop Review 3) In-Studio Work.
Math 3360: Mathematical Imaging Prof. Ronald Lok Ming Lui Department of Mathematics, The Chinese University of Hong Kong Lecture 9: More about Discrete.
Chapter 5: Neighborhood Processing
Digital Image Processing (Digitaalinen kuvankäsittely) Exercise 2
Machine Vision ENT 273 Image Filters Hema C.R. Lecture 5.
Image Enhancement ارتقاء تصویر Enhancement Spatial Domain Frequency Domain.
Image Subtraction Mask mode radiography h(x,y) is the mask.
Lecture # 16 GIMP and Image Editing. GIMP by Example: Restoring Pictures.
Image Manipulation CSC361/661 – Digital Media Spring 2002 Burg/Wong.
Intelligent Vision Systems ENT 496 Image Filtering and Enhancement Hema C.R. Lecture 4.
Sejong Univ. CH3. Area Processes Convolutions Blurring Sharpening Averaging vs. Median Filtering.
WORKING WITH SELECTIONS MASKS and CHANNELS 3D IMAGES LAYER BASICS PHOTO.
Chapter 3: Basic Photo Corrections Photoshop: CIB Mr. Peña.
Digital Image Processing Part 2 Contrast processing.
Frequency Domain By Dr. Rajeev Srivastava. Image enhancement in the frequency domain is straightforward. We simply compute the Fourier transform of the.
Image Enhancement in Spatial Domain Presented by : - Mr. Trushar Shah. ME/MC Department, U.V.Patel College of Engineering, Kherva.
Digital Image Processing Part 3 Spatial Domain Processing.
PhotoShop: Brightness/Density Control Overall Control: Adjustment Layers –Levels –Brightness/Contrast Local Control: No Layers –Dodging = Lightens an area.
Image Enhancement by Spatial Domain Filtering
Image Processing Intro2CS – week 6 1. Image Processing Many devices now have cameras on them Lots of image data recorded for computers to process. But.
ITEC2110, Digital Media Chapter 3 Digital Image Processing 1 GGC -- ITEC Digital Media.
V2.2 Boo Virk GIMP Tutorial v2.2 Boo Virk
Environmental Remote Sensing GEOG 2021
Spatial Image Enhancement
Image Subtraction Mask mode radiography h(x,y) is the mask.
"Digital Media Primer" Yue-Ling Wong, Copyright (c)2013 by Pearson Education, Inc. All rights reserved.
The Chinese University of Hong Kong
Dr. Jim Rowan ITEC 2110 Bitmapped Images
- photometric aspects of image formation gray level images
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
Computer Vision Lecture 3: Digital Images
Lecture 3 (2.5.07) Image Enhancement in Spatial Domain
Sharpening..
Y/I/Q channel blurring with 5x5 mean filter
Linear Operations Using Masks
Color Image Processing
Fourier Transforms.
The Image The pixels in the image The mask The resulting image 255 X
Intensity Transform Contrast Stretching Y ← u0+γ*(Y-u)/s
Image Filtering with GLSL
Lecture 7 Spatial filtering.
Image Enhancement in Spatial Domain: Point Processing
Presentation transcript:

Resolution 244 243 230 242 255 Resolution: 6 x 4

easy if we use exact multiples Scaling an Image 244 243 230 242 255 ...reduces image quality why? easy if we use exact multiples complicated if we don't

Selections and Masks A mask is another name for a selection A mask is a subset of an image’s pixels This allows us to restrict our processing to a subset of pixels How do we select regions in GIMP?

Histograms A graph of pixel color frequencies

Pixel Point Processing 244 243 230 242 255 243 242 229 241 254 We change each pixel individually Can be used for lightening, darkening, contrast adjustments

Pixel Group Processing 244 243 230 242 255 1/9*243 + 1/9*242 + 1/9*0 + 1/9*230 + 1/9*242 + 1/9*0 + 1/9*230 + 1/9*230 + 1/9*0 = 158 1/9 convolution mask We change each pixel based on its neighbors Can be used for blurring, sharpening, complex filters