Understanding Digital Image Processing: Pixels, Intensity, and Image Formation

what's up everybody welcome to the first lecture of the dip series this is anushri and today i'm

going to teach you digital image processing now if you're new to our channel i'm not

going to ask you to hit the like and subscribe button all right just do so if you understand the concept so

let's get started now we have an x and y coordinate system here

and suppose we draw an image okay which kind of looks like an apple now we will draw the darker regions of

this image with black okay and the lighter regions with blue

let's also write down the numbers here now i'm saying that this image can be represented

as a function of x and y how is that now if we look at these points

okay if you look at these points then each of these points have certain intensity

okay if you look at f of 0 0 it doesn't have any intensity it doesn't have any value so it's 0

but if you look at this point f of 2 2 it has a high intensity why because that at that point we have a black color

similarly if you look at this point f of 3 2 it has a lesser value why because it is represented with blue

color which is lighter shade okay so each of these points have certain intensity values

okay so if we want to represent this image then it can be done with the help of a

matrix so suppose we write f of x comma y is equal to f of 0 0

0 1 0 2 and so on f of 1 0 1 1 1 2 and so on

2 0 2 1 2 2 and so on so in this way this image can be represented with the

help of this matrix where each of these matrix values have certain intensities

okay and in what range will they have these intensities it will be in the range of 0 to 255

if this is an 8-bit grayscale image okay so that's it for the representation part

let's move on to the definitions what is the digital image a digital image is an image composed of

picture elements also known as pixels each with finite discrete quantities of numeric representation

for its intensity or gray level that is an output from its 2d functions fed as input by its spatial coordinates

denoted by x and y on the x axis and y axis respectively

so exactly what i explained to you with the help of this image this image can be represented as a

function of f of x and y where x and y are the spatial coordinates okay

the amplitude of the f is called intensity or gray level at the point x comma y now this term grey level is very

important and it is mainly used for image classification

all right digital image processing it is the use of computer to process images with the help of an algorithm

easy right so based on different applications we have divided this entire procedure

into different levels suppose our application is noise removal or image sharpening

and that is when we use low level processes because here the inputs and outputs both are images

suppose our application is object recognition or segmentation so in that case we need certain

attributes of the input image and that is why we use mid-level processes because here

the outputs are attributes extracted from input images if our application is seen understanding

or autonomous navigation then that is where we use high-level processes because your

an ensemble of recognition of individual objects is used okay so now what is a pixel it is the

smallest building block of a picture represented on the screen you remember this image which we had

so here each of these points is a pixel okay it is the building block of an image with the help of these pixels

this entire image can be created and these pixels have certain intensity values

if we have an 8 bit grayscale image then these values range from 0 to 255 where 0 will represent lesser intensity

or 0 value and 255 will represent highest intensity okay those are the definitions now we will

move on to a simple image formation model okay so we know that any object

in the real world can be expressed with the help of a 2d image so for an image to be produced there

should be a light source illuminating the object right when we have a light source that

is when we'll be able to see the object so when an image is generated from a physical process

its values are proportional to the energy radiated by a physical source right so these are some things which we

know and also the intensity or amplitude of f at spatial coordinates is a positive

scalar quantity whose physical meaning is determined by the source of the image

so based on the above points we can say that the function must be non-zero and finite which means that it should

lie between zero and infinity now the two factors which affect the formation of an image

are the amount of source illumination incident on the scene and the amount of illumination reflected

by the objects in the scene okay so these illumination and reflectance components

can be denoted by i of x comma y and r of x comma y respectively so these two functions combine to form f

of x comma y where f of x comma y is equal to i into x comma y into

r into x comma y so we can write this equation again where f represents the intensity

at the point x comma y i represents the illumination of the point x comma y

and r represents the reflectance or transmissivity at the point x comma y okay now

reflectance is bounded by zero and one why because zero represents total absorption

and one represents total reflectance but that is not the case with illumination at the point x comma y okay

so that is why it is bounded by zero and infinity the nature of i of x comma y is

determined by the illumination source whereas the nature of

r of x comma y is determined by the characteristics of the imaged objects okay so that was it with the simple

image formation model