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)