Stretch the dynamic range of the given 8-bit grayscale image using MATLAB

Introduction—

MATLABSolutions demonstrate In this task we are going to design The The above figure of the resultant of the dynamic stretch for the ‘moon.tif’ image which is in grayscale we have taken the value of algorithm will give the above resultant result we can see that the dark and bright pixel gives the higher number of pixel count whereas all the pixel values between them gives the equalized count of pixel. We can change the parameter in to the algorithm to get the desired result according to the requirement. For this task we were creating the algorithm for the dynamic range stretching of the given 8-bit grayscale image.

The above image is the histogram equalization of the original image which equalize each pixel count by finding the probability of each pixel value for the 8bit intensity range which is [0 255]. From this result we can see that the half portion is getting equalize around 8000 and half portion of the intensity will give the equalize around 3000. By comparing the both the image the dynamic stretch gives us the better visualization whereas the histogram equalization give noise in the picture we also visualize the same as we compare the two histograms of both the images results.

Test Filter Function

                        % Clear data and figres
clc
clear
close all
% Read Image
Img=imread('strawberries_fullcolor.tif');
% Convert to grayscale
Img=rgb2gray(Img);
% Create 3x3 Average intensity mask
Filter1 = fspecial('average', [3 3]);
% Apply Filter
Img_Avg = imfilter(Img,Filter1);
% Cutoff Frequency value
D=0.08;
% Type of filter
Type="Low";
% Apply Filter to Original Image
G=FilterImage(Img_Avg,Type,D);

An image is processed in the frequency domain via frequency filters. The image is first transformed using Fourier analysis, then multiplied by the filter function, and finally turned once more into the spatial domain. While attenuating low frequencies improves the edges, attenuating high frequencies produces a smoother image in the spatial domain. From the above results we can see that we are successfully implement the filtering function in MATLAB the function plot original image as well as filtered image on same figure as shown in the above figure. All frequency filters are also implementable in the spatial domain, and provided the desired filter effect has a straightforward kernel, filtering in the spatial domain is computationally more efficient. If there isn't a simple kernel in the spatial domain, frequency filtering is more appropriate and might be more effective.