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.