Super Resolution Image Reconstruction Through

Bregman Iteration using MorphologicRegularization

Major ProjectBy

D.Jyothi:11pu1a0463K.Madhusree:11pu1a0467K.R.S.Swetha:11pu1a079

At IETE under the guidence of Kishore Kumar Sir

(assistant professor OU)

AbstractMultiscale morphological operators are studied ex-

tensively in the literature for image processing and feature extrac-

tion purpose. In this paper we model a non-linear regularizationmethod based on multiscale morphology for edge-preservingsuper resolution (SR) image reconstruction. We formulate SRimage reconstruction problem as a de-blurring problem and

then solve the inverse problem using Bregman iterations. Theproposed algorithm can suppress inherent noise generated during

low-resolution (LR) image formation as well as during SR imageestimation efficiently. Experimental results show the effectivenessof the proposed regularization and reconstruction method for SR

INTRODUCTION

IMAGE: An image is a two-dimensional function f(x,y), where x and y are the

spatial (plane) coordinates, and the amplitude of f at any pair of coordinates (x,y) is called the intensity of the image at that level.

If x,y and the amplitude values of f are finite and discrete quantities, we call the image a digital image. A digital image is composed of a

finite number of elements called pixels, each of which has a particular location and value.

PIXEL: The smallest addressable element in an all point addressable display device; so it is the smallest controllable element of a

picture represented on the screen.

RESOLUTION Resolution is the capability of the sensor to observe or

measure the smallest object clearly with distinct boundaries. There is a difference between the resolution and a pixel. A

pixel is actually a unit of the digital image. Resolution depends upon the size of the pixel. With a given lens setting the

smaller the size of the pixel, the higher the resolution will be and the clearer the object in the image will be. Images having smaller pixel sizes might consist of more pixels. The number of

pixels correlates to the amount of information within the image.

SUPER RESOLUTIONSuper resolution (SR) is a class of techniques that enhance the

resolution of an imaging system.Super-resolution (SR) is the process of combining a sequence

of low resolution images in order to produce a higher resolution image or sequence.

ABOUT PROJECT

Our project consists of three parts………..

1.Morphology 2.Edge Detection

3.Super Resolution

MORPHOLOGY Morphological image processing is a collection of non-linear

operations related to the shape or morphology of features in an image.

The basic idea is to probe an image with a template shape, which is called structuring element, to quantify the manner in which

the structuring element fits within a given image.Morphology can be basically categorised into two tecniques:

1.Dilation2.Erosion

DILATION: The value of the output pixel is the maximum value of all the pixels in the input pixel's neighborhood. In a binary image, if

any of the pixels is set to the value 1, the output pixel is set to 1.

EROSION: The value of the output pixel is the minimum value of all the pixels in the input pixel's neighborhood. In

a binary image, if any of the pixels is set to 0, the output pixel is set to 0.