edge detection
TRANSCRIPT
EDGE DETECTION
INTRODUCTION: Edges are significant local changes of intensity in
an image, typically occur on the boundary between two different regions in an image.
Edge detection is an image processing technique for finding the boundaries of objects within images.
It is the name for a set of mathematical methods which aim at identifying points in a digital image at which the image brightness changes sharply.
GOAL OF EDGE DETECTION
Produce a line drawing of a scene from an image of that scene.
Important features can be extracted from the edges of an image (e.g., corners,lines, curves).
These features are used by higher-level computer vision algorithms (e.g., recognition)
METHODS
Canny Edge Detector
Roberts edge detector
Prewitt edge detector
Sobel edge detector
Second order derivatives
Original Image
Filtered Image
NOISE IN IMAGE
IMAGE NOISE
Image noise is random variation of brightness or color information in images.
It is an undesirable by-product of image capture that adds spurious and extraneous information.
Noise is introduced in the image at the time of image acquisition or transmission.
TYPES OF IMAGE NOISE
Salt and pepper noise
Gaussian noise
Speckle noise
Poisson noise
SALT AND PEPPER NOISE
It is known as shot noise, impulse noise or Spike noise.
An image containing salt-and-pepper noise will have dark pixels in bright regions and bright pixels in dark regions.
Reasons: 1. Memory cell failure. 2. Malfunctioning pixel elements camera’s
sensor.
SALT AND PEPPER NOISE
Original Image without Noise
Image with Salt & Pepper Noise
GAUSSIAN NOISE
Also called Electronic circuit noise or Sensor noise caused by poor illumination.
It is caused by random fluctuations in the signal.
This noise has a probability density function of the normal distribution.
GAUSSIAN NOISE
Original Image without Noise
Image with Gaussian Noise
SPECKLE NOISE
Speckle is a granular 'noise' that inherently exists in and degrades the quality of the active radar and medical ultrasound images.
Speckle noise can be modeled by random values multiplied by pixel values of an image.
Results from random fluctuations in the return signal from an object.
SPECKLE NOISE
Original Image without Noise
Image with Speckle Noise
POISSON NOISE Poisson noise is also known as Photon
noise.
Poisson noise is a basic form of uncertainty associated with the measurement of light.
Its expected magnitude is signal-dependent and constitutes the dominant source of image noise except in low-light conditions.
POISSON NOISE
Original Image without Noise
Image with Poisson Noise
PSNR VALUES OF SOME NOISY IMAGES
Salt & Pepper noise,30% PSNR= 20.6652
Gaussian Noise, 30% PSNR= 19.6715
PSNR VALUES OF SOME NOISY IMAGES
Speckle Noise, 20% PSNR= 22.6618
Image with Poisson NoisePSNR= 27.1939
DE-NOISING Denoising (noise reduction) is to remove
noise as much as possible while preserving useful information as much as possible.
IMAGE DE-NOISING
Mean Filter
Median Filter
Order Statistics Filter
DE-NOISING USING KINGSBURY TOOLBOX Uses Dual-Tree Complex Wave
Transform(DT CWT)
This technique uses two real filters
Compute forward DTCWT
Compute inverse DTCWT
Extract output image
PSNR VALUES AFTER DE-NOISING
Salt & Pepper noise,30% PSNR= 20.6652
After De-Noising PSNR= 21.2236
THANK YOU
Submitted by:-
Ayush Agrawal(5th sem)
Pratik Jain(3rd sem)
Nishant Sharma(3rd sem)