Untitled

1. Intensity Level Slicing with Highlighting a Specific Range
matlab
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% Intensity level slicing (highlighting a specific range)
img = imread('image.jpg');
gray_img = double(0.2989 * img(:, :, 1) + 0.5870 * img(:, :, 2) + 0.1140 * img(:, :, 3));

% Parameters for intensity level slicing
low_threshold = 100;  % Lower bound of the intensity range
high_threshold = 200; % Upper bound of the intensity range
highlight_value = 255; % Value to set for pixels within the range

sliced_img = zeros(size(gray_img));
for i = 1:size(gray_img, 1)
    for j = 1:size(gray_img, 2)
        if gray_img(i, j) >= low_threshold && gray_img(i, j) <= high_threshold
            sliced_img(i, j) = highlight_value; % Highlight the pixel
        else
            sliced_img(i, j) = gray_img(i, j); % Keep the original value
        end
    end
end

imshow(uint8(sliced_img));
title('Intensity Level Slicing (Highlighting Specific Range)');


2. Intensity Level Slicing with Binary Mask
matlab
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% Intensity level slicing with binary mask approach
img = imread('image.jpg');
gray_img = double(0.2989 * img(:, :, 1) + 0.5870 * img(:, :, 2) + 0.1140 * img(:, :, 3));

% Parameters for intensity level slicing
low_threshold = 100;  % Lower bound of the intensity range
high_threshold = 200; % Upper bound of the intensity range
inside_value = 255;   % Value for pixels within the range
outside_value = 0;    % Value for pixels outside the range

binary_sliced_img = zeros(size(gray_img));
for i = 1:size(gray_img, 1)
    for j = 1:size(gray_img, 2)
        if gray_img(i, j) >= low_threshold && gray_img(i, j) <= high_threshold
            binary_sliced_img(i, j) = inside_value; % Set to high value
        else
            binary_sliced_img(i, j) = outside_value; % Set to low value
        end
    end
end

imshow(uint8(binary_sliced_img));
title('Intensity Level Slicing (Binary Mask Approach)');


1. Reading and Displaying Images using subplot
matlab
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% Read and display images using subplot
img = imread('image.jpg');
subplot(1, 2, 1);
imshow(img);
title('Original Image');

% Convert to grayscale manually
gray_img = 0.2989 * img(:, :, 1) + 0.5870 * img(:, :, 2) + 0.1140 * img(:, :, 3);
subplot(1, 2, 2);
imshow(uint8(gray_img));
title('Grayscale Image');

2. Converting Colored Image into Grayscale Without Using In-Built Functions
matlab
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% Convert color image to grayscale
img = imread('image.jpg');
gray_img = 0.2989 * img(:, :, 1) + 0.5870 * img(:, :, 2) + 0.1140 * img(:, :, 3);
imshow(uint8(gray_img));
title('Grayscale Image');

3. Sampling and Quantization
matlab
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% Sampling and quantization example
img = imread('image.jpg');
gray_img = 0.2989 * img(:, :, 1) + 0.5870 * img(:, :, 2) + 0.1140 * img(:, :, 3);
gray_img = uint8(gray_img);

% Downsampling by a factor of 2
sampled_img = gray_img(1:2:end, 1:2:end);
imshow(sampled_img);
title('Sampled Image');

4. Negative of an Image
matlab
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% Image negative transformation
img = imread('image.jpg');
gray_img = 0.2989 * img(:, :, 1) + 0.5870 * img(:, :, 2) + 0.1140 * img(:, :, 3);
negative_img = 255 - gray_img;
imshow(uint8(negative_img));
title('Negative Image');

5. Log and Power-Law Correction
matlab
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% Log and power-law (gamma) correction
img = double(imread('image.jpg'));
c = 1; % Constant for log transformation
log_img = c * log(1 + img);
log_img = uint8(255 * mat2gray(log_img));
imshow(log_img);
title('Log Transformation');

% Power-law (gamma) transformation
gamma = 2.0; % Choose gamma value
power_img = c * (img .^ gamma);
power_img = uint8(255 * mat2gray(power_img));
figure, imshow(power_img);
title('Power-Law (Gamma) Transformation');

6. Thresholding
matlab
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% Thresholding without using built-in functions
img = imread('image.jpg');
gray_img = 0.2989 * img(:, :, 1) + 0.5870 * img(:, :, 2) + 0.1140 * img(:, :, 3);
threshold = 128; % Choose a threshold value
binary_img = gray_img > threshold;
imshow(binary_img);
title('Thresholded Image');

8. Contrast Stretching
matlab
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% Contrast stretching
img = imread('image.jpg');
gray_img = double(0.2989 * img(:, :, 1) + 0.5870 * img(:, :, 2) + 0.1140 * img(:, :, 3));
min_val = min(gray_img(:));
max_val = max(gray_img(:));
stretched_img = (gray_img - min_val) * (255 / (max_val - min_val));
imshow(uint8(stretched_img));
title('Contrast Stretched Image');

9. Piecewise Linear Transformation
matlab
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% Piecewise linear transformation
img = imread('image.jpg');
gray_img = double(0.2989 * img(:, :, 1) + 0.5870 * img(:, :, 2) + 0.1140 * img(:, :, 3));
r1 = 50; s1 = 0;
r2 = 200; s2 = 255;
transformed_img = zeros(size(gray_img));
for i = 1:size(gray_img, 1)
    for j = 1:size(gray_img, 2)
        if gray_img(i, j) < r1
            transformed_img(i, j) = (s1 / r1) * gray_img(i, j);
        elseif gray_img(i, j) <= r2
            transformed_img(i, j) = ((s2 - s1) / (r2 - r1)) * (gray_img(i, j) - r1) + s1;
        else
            transformed_img(i, j) = ((255 - s2) / (255 - r2)) * (gray_img(i, j) - r2) + s2;
        end
    end
end
imshow(uint8(transformed_img));
title('Piecewise Linear Transformed Image');

10. Histogram Equalization Without Using In-Built Functions
matlab
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% Histogram equalization manually
img = imread('image.jpg');
gray_img = double(0.2989 * img(:, :, 1) + 0.5870 * img(:, :, 2) + 0.1140 * img(:, :, 3));
[m, n] = size(gray_img);
histogram = zeros(1, 256);
for i = 1:m
    for j = 1:n
        histogram(gray_img(i, j) + 1) = histogram(gray_img(i, j) + 1) + 1;
    end
end
cdf = cumsum(histogram) / (m * n);
equalized_img = uint8(255 * cdf(gray_img + 1));
imshow(equalized_img);
title('Histogram Equalized Image');

11. Smoothing in Spatial Domain Using Simple Average, Weighted Average, Median Filter

1. Smoothing Using Simple Average Filter
matlab
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% Read the input image
img = imread('image.jpg');
if size(img, 3) == 3
    gray_img = rgb2gray(img); % Convert to grayscale if it is a color image
else
    gray_img = img; % If already grayscale, use it directly
end

% Convert to double for processing
gray_img = double(gray_img);

% Define a simple average filter (3x3 kernel)
average_filter = ones(3) / 9;

% Apply the simple average filter
[m, n] = size(gray_img);
padded_img = padarray(gray_img, [1, 1], 'replicate');
filtered_img = zeros(m, n);

for i = 2:m+1
    for j = 2:n+1
        % Extract the 3x3 window
        window = padded_img(i-1:i+1, j-1:j+1);
        % Apply the average filter
        filtered_img(i-1, j-1) = sum(sum(window .* average_filter));
    end
end

% Display the result
imshow(uint8(filtered_img));
title('Smoothed Image (Simple Average Filter)');
2. Smoothing Using Weighted Average Filter
matlab
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% Read the input image
img = imread('image.jpg');
if size(img, 3) == 3
    gray_img = rgb2gray(img); % Convert to grayscale if it is a color image
else
    gray_img = img; % If already grayscale, use it directly
end

% Convert to double for processing
gray_img = double(gray_img);

% Define a weighted average filter (3x3 kernel)
weighted_filter = [1 2 1; 2 4 2; 1 2 1] / 16;

% Apply the weighted average filter
[m, n] = size(gray_img);
padded_img = padarray(gray_img, [1, 1], 'replicate');
filtered_img = zeros(m, n);

for i = 2:m+1
    for j = 2:n+1
        % Extract the 3x3 window
        window = padded_img(i-1:i+1, j-1:j+1);
        % Apply the weighted average filter
        filtered_img(i-1, j-1) = sum(sum(window .* weighted_filter));
    end
end

% Display the result
imshow(uint8(filtered_img));
title('Smoothed Image (Weighted Average Filter)');
3. Smoothing Using Median Filter
matlab
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% Read the input image
img = imread('image.jpg');
if size(img, 3) == 3
    gray_img = rgb2gray(img); % Convert to grayscale if it is a color image
else
    gray_img = img; % If already grayscale, use it directly
end

% Convert to double for processing
gray_img = double(gray_img);

% Apply the median filter (3x3 window)
[m, n] = size(gray_img);
padded_img = padarray(gray_img, [1, 1], 'replicate');
filtered_img = zeros(m, n);

for i = 2:m+1
    for j = 2:n+1
        % Extract the 3x3 window
        window = padded_img(i-1:i+1, j-1:j+1);
        % Apply the median filter
        filtered_img(i-1, j-1) = median(window(:));
    end
end

% Display the result
imshow(uint8(filtered_img));
title('Smoothed Image (Median Filter)');

12. Sharpening in Spatial Domain Using Laplacian

% Read the input image
img = imread('image.jpg');
if size(img, 3) == 3
    gray_img = rgb2gray(img); % Convert to grayscale if it is a color image
else
    gray_img = img; % If already grayscale, use it directly
end

% Convert the image to double for processing
gray_img = double(gray_img);

% Define the Laplacian filter
laplacian_filter = [0 -1 0; -1 4 -1; 0 -1 0];

% Apply the Laplacian filter using convolution
[m, n] = size(gray_img);
padded_img = padarray(gray_img, [1, 1], 'replicate'); % Pad the image to handle borders
sharpened_img = zeros(m, n);

for i = 2:m+1
    for j = 2:n+1
        % Extract the 3x3 window
        window = padded_img(i-1:i+1, j-1:j+1);
        % Apply the Laplacian filter
        sharpened_img(i-1, j-1) = sum(sum(window .* laplacian_filter));
    end
end

% Combine the original image with the Laplacian result to sharpen
sharpened_img = gray_img - sharpened_img;

% Clip values to stay within the valid range [0, 255]
sharpened_img = max(min(sharpened_img, 255), 0);

% Display the original and sharpened images
subplot(1, 2, 1);
imshow(uint8(gray_img));
title('Original Image');

subplot(1, 2, 2);
imshow(uint8(sharpened_img));
title('Sharpened Image (Laplacian)');


13. Removing Noise from an Image

 Removing Noise Using Median Filter
matlab
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% Read the input image
img = imread('image.jpg');
if size(img, 3) == 3
    gray_img = rgb2gray(img); % Convert to grayscale if it is a color image
else
    gray_img = img; % If already grayscale, use it directly
end

% Add noise to the image (for demonstration purposes)
noisy_img = imnoise(gray_img, 'salt & pepper', 0.02);

% Apply median filter (3x3 window)
[m, n] = size(noisy_img);
filtered_img = zeros(m, n);
for i = 2:m-1
    for j = 2:n-1
        % Extract the 3x3 window
        window = noisy_img(i-1:i+1, j-1:j+1);
        % Find the median value
        filtered_img(i, j) = median(window(:));
    end
end

% Display the original and filtered images
subplot(1, 2, 1);
imshow(noisy_img);
title('Noisy Image');

subplot(1, 2, 2);
imshow(uint8(filtered_img));
title('Filtered Image (Median Filter)');