Basis beeldverwerking (8D040) dr. Andrea Fuster dr. Anna Vilanova Prof.dr. Marcel Breeuwer Noise and Filtering.

Slides:



Advertisements
Similar presentations
Basis beeldverwerking (8D040) dr. Andrea Fuster dr. Anna Vilanova Prof.dr.ir. Marcel Breeuwer Filtering.
Advertisements

Spatial Filtering (Chapter 3)
ECE 472/572 - Digital Image Processing Lecture 7 - Image Restoration - Noise Models 10/04/11.
Digital Image Processing Chapter 5: Image Restoration.
Digital Image Processing
Image Restoration 影像復原 Spring 2005, Jen-Chang Liu.
5. 1 Model of Image degradation and restoration
Digital Image Processing Chapter 5: Image Restoration.
1 Lecture 12 Neighbourhood Operations (2) TK3813 DR MASRI AYOB.
1 Linear image transforms Let’s start with a 1-D image (a “signal”): A very general and useful class of transforms are the linear transforms of f, defined.
Image Restoration.
1 Image filtering Images by Pawan SinhaPawan Sinha.
Linear filtering.
Chapter 5 Image Restoration. Preview Goal: improve an image in some predefined sense. Image enhancement: subjective process Image restoration: objective.
Basic Image Processing January 26, 30 and February 1.
Digital Image Processing Lecture 4 Image Restoration and Reconstruction Second Semester Azad University Islamshar Branch
Presentation Image Filters
Machine Vision ENT 273 Image Filters Hema C.R. Lecture 5.
Image Restoration The main aim of restoration is to improve an image in some predefined way. Image Enhancement is a subjective process whereas Image restoration.
© by Yu Hen Hu 1 ECE533 Digital Image Processing Image Restoration.
Spatial Filtering: Basics
Chapter 5 Image Restoration.
Image Restoration and Reconstruction (Noise Removal)
Computer Vision - Restoration Hanyang University Jong-Il Park.
Digital Image Processing Image Enhancement Part IV.
DIGITAL IMAGE PROCESSING Instructors: Dr J. Shanbehzadeh M.Gholizadeh M.Gholizadeh
Basis beeldverwerking (8D040) dr. Andrea Fuster Prof.dr. Bart ter Haar Romeny dr. Anna Vilanova Prof.dr.ir. Marcel Breeuwer Convolution.
Digital Image Processing
Lecture 03 Area Based Image Processing Lecture 03 Area Based Image Processing Mata kuliah: T Computer Vision Tahun: 2010.
AdeptSight Image Processing Tools Lee Haney January 21, 2010.
Image processing Fourth lecture Image Restoration Image Restoration: Image restoration methods are used to improve the appearance of an image.
انجمن دانشجویان ایران – مرجع دانلود کتاب ، نمونه سوال و جزوات درسی
Image Restoration Chapter 5.
Digital Image Processing Lecture 10: Image Restoration March 28, 2005 Prof. Charlene Tsai.
Image Restoration.
Digital Image Processing Lecture : Image Restoration
Chapter 5: Neighborhood Processing
Linear filtering. Motivation: Noise reduction Given a camera and a still scene, how can you reduce noise? Take lots of images and average them! What’s.
Course Website: Digital Image Processing Image Enhancement (Spatial Filtering 1)
Lecture 5 Mask/Filter Transformation 1.The concept of mask/filters 2.Mathematical model of filtering Correlation, convolution 3.Smoother filters 4.Filter.
Machine Vision ENT 273 Image Filters Hema C.R. Lecture 5.
Image Restoration Fasih ur Rehman. –Goal of restoration: improve image quality –Is an objective process compared to image enhancement –Restoration attempts.
Digital Image Processing Lecture 10: Image Restoration
8-1 Chapter 8: Image Restoration Image enhancement: Overlook degradation processes, deal with images intuitively Image restoration: Known degradation processes;
Spatial Filtering.
Ch5 Image Restoration CS446 Instructor: Nada ALZaben.
Course 2 Image Filtering. Image filtering is often required prior any other vision processes to remove image noise, overcome image corruption and change.
Intelligent Vision Systems ENT 496 Image Filtering and Enhancement Hema C.R. Lecture 4.
Chapter 5 Image Restoration.
By Dr. Rajeev Srivastava
Instructor: Mircea Nicolescu Lecture 5 CS 485 / 685 Computer Vision.
Filtering (II) Dr. Chang Shu COMP 4900C Winter 2008.
Image Restoration. Image restoration vs. image enhancement Enhancement:  largely a subjective process  Priori knowledge about the degradation is not.
6/10/20161 Digital Image Processing Lecture 09: Image Restoration-I Naveed Ejaz.
Lecture 10 Chapter 5: Image Restoration. Image restoration Image restoration is the process of recovering the original scene from the observed scene which.
Image Processing Lab Section 28/3/2016 Prepared by Mahmoud Abdelsatar Demonstrator at IT Dep. Faculty of computers and information Assuit University.
Image Restoration : Noise Reduction
Basis beeldverwerking (8D040) dr. Andrea Fuster dr. Anna Vilanova Prof
Digital Image Processing Lecture 10: Image Restoration
IMAGE PROCESSING IMAGE RESTORATION AND NOISE REDUCTION
Image Pre-Processing in the Spatial and Frequent Domain
ECE 692 – Advanced Topics in Computer Vision
Digital Image Processing
DIGITAL IMAGE PROCESSING
LỌC NHIỄU trong miền không gian
Digital Image Processing
Image Restoration - Focus on Noise
Basic Image Processing
Spatial filtering 3x3 kernel Definition Transformation or set of
Presentation transcript:

Basis beeldverwerking (8D040) dr. Andrea Fuster dr. Anna Vilanova Prof.dr. Marcel Breeuwer Noise and Filtering

Contents Noise Mean Filters Order-statistic filters Median Alpha-trimmed 2

Gaussian Noise Gaussian noise follows a Gaussian distribution Average = Standard deviation = Good approximation of noise that occurs in practical cases.

Additive Gaussian Noise Example

Impulse Noise Model Bipolar impulse noise follows the following distribution If or is zero, we have unipolar impulse noise If both are nonzero, and almost equal, this is also called salt-and-pepper noise

Impulse Noise Impulses can be positive and negative are often very large can go out of the range of the image appear as black and white dots, saturated peaks

Impulse Noise Example

Contents Noise Mean Filters Order-statistic filters Median Alpha-trimmed 8

Mean Filters Blurring used to smooth images by e.g. convolution with smoothing kernel Can be used to suppress noise 9

Arithmetic Mean Filter Arithmetic mean filter replaces the current pixel with a uniform weighted average of the neighbourhood 10

Geometric Mean Filter Like arithmetic mean filter, but loses less detail 11

Harmonic Mean Filter Works well for Gaussian noise Works well for salt noise, but fails for pepper noise 12

Contraharmonic Mean Filter Is very effective in eliminating Salt-and-Pepper noise Q is the order of the filter 13

Contraharmonic Mean Filter If Q=0, this is the arithmetic mean filter If Q=-1, this is the harmonic mean filter If Q<0, salt noise is eliminated If Q>0, pepper noise is eliminated For examples, see book page

Contents Noise Mean Filters Order-statistic filters Median Alpha-trimmed 15

Order-statistic filters Result is based on ordering pixel values in the neighbourhood Examples: median, max, min filters 16 median min max

Contents Noise Mean Filters Order-statistic filters Median Alpha-trimmed 17

Median Filter Replaces value of a pixel by the median of its neighbourhood 18

Median filter Can be used to reduce random noise Less blurring than linear smoothing filter Very effective for impulse noise (salt-and-pepper noise) 19 Mean filtering 3x3Mean filtering 9x9Median filtering 3x3Median filtering 9x9

Max and min filters Max filter: −Take maximum of ordered pixel values −Find brightest points of an image (so: filters pepper noise) Min filter: −Take minimum of ordered pixel values −Find darkest points of an image (filters salt noise) 20

21 Original Salt-and-Pepper noise Median filteredMin filtered Max filtered 1 st quartile filtered 3 rd quartile filtered Midpoint filtered

Contents Noise Mean Filters Order-statistic filters Median Alpha-trimmed 22

Alpha-trimmed mean filter Delete d/2 lowest and d/2 highest values of from neighbourhood remains d=0 arithmetic mean filter d=mn-1 median filter 23

Alpha-trimmed mean filter works good for combination of S&P noise and Gaussian noise 24 Image with S&P noise and Gaussian noise Alpha-trimmed image (5x5, d=6) Median filtered image (5x5)