Digital Image Processing

1. Digital Image Processing:
Image Enhancement in Spatial
Domain
Nidhi Sharma

2. Image Enhancement
 Image enhancement in the spatial domain is a technique used to improve the
visual quality of digital images by directly manipulating the pixel values.
 It involves modifying the image pixel by pixel, considering their spatial
relationships within the image.
 Image enhancement approaches fall into two broad categories:
 Spatial domain methods
 Point processes
 Area processes
 Frame process
 Frequency domain methods

3. Image Enhancement: Spatial Domain
 The term spatial domain refers to the aggregate of pixels composing an
image.
 Spatial domain methods are procedures that operate directly on these pixels.
Spatial domain processes will be denoted by the expression
g (x, y) = T [f (x, y)]
where f(x, y) is the input image, g(x, y) is the processed image, and T is an
operator on f, defined over some neighborhood of (x, y).
For example:
T can operate on a set of input images, such as performing the pixel-by-pixel sum
of K images for noise reduction.

4. Spatial Image Enhancement
 Single-pixel operation (Intensity Transformation)
Negative Image, contrast stretching etc.
 Neighbourhood operations
Averaging filter, median filtering etc.
 Geometric spatial transformations
Scaling, Rotation, Translations et

5. Single Pixel Operations

6. Neighbourhood Operations

7. Geometric Spatial Operators

8. Averaging
 Often done to improve SNR.
avgI = (img1+img2+img3+img4+img5)/5;
Remember that the data is Uint8 type…..which may saturate the sum values to 255!
So
avgI = (im2double(img1)+im2double(img2)+ im2double(img3)+ im2double(img4)+
im2double(img5))/5;
Im2double also rescales range to [0,1]

9. Resizing and Transforming
 Use ‘montage’ to see and compare image side by side
Montage ({image1, image2})
avgGray = im2gray (avgI);
avgIreduced = imresize( avgI, 0.75);
avgIsquared = imresize( avgI, 2000, 2000);
avgIrotated = imrotate( avgI, 30, ‘crop’);

10.  Imwrite( avgI, “filename.png”); % to save image as a given file name
 Imwrite( avgI, “filename.jpg”, “Quality”, 80); % to save image as a given file name

11. Some Basic Intensity Transformation
Functions
 Image Negatives
s = L – 1 – r
S is the output intensity value
L is the highest intensity levels
r is the input intensity value
 Particularly suited for enhancing white or gray detail embedded in dark
regions of an image, especially when the black areas are dominant in size

12. Some Basic Intensity Transformation
Functions
 Log Transformations
 s = c log(1 + r) where , c is a constant
 It maps a narrow range of low intensity values in the input into a wide range of output
levels
 The opposite is true of higher values of input levels
 It expands the values of dark pixels in an image while compressing the higher level
values
 It compresses the dynamic range of images with large variations in pixel values

13. % Read the input image
inputImage = imread('input_image.jpg'); % Replace 'input_image.jpg' with your image file name and
extension
% Convert the input image to double precision for accurate calculations
inputImage = im2double(inputImage);
% Perform log transformation
c = 1; % Constant value for scaling
outputImage = c * log(1 + inputImage);
% Rescale the output image to the range [0, 1] for display
outputImage = (outputImage - min(outputImage(:))) / (max(outputImage(:)) - min(outputImage(:)));
% Display the input and output images figure;
subplot(1, 2, 1);
imshow(inputImage);
title('Input Image');
subplot(1, 2, 2);
imshow(outputImage);
title('Log Transformed Image');
% Save the output image
imwrite(outputImage, 'output_image.jpg'); % Replace 'output_image.jpg' with your desired output image
file name and extension

14. Example:

15. Some Basic Intensity Transformation
Functions
 Power Law (Gamma) Transformations
 s = c rγ
 c and γ are both positive constants
 With fractional values(0<γ<1) of gamma map a narrow range of dark input
values into a wider range of output values, with the opposite being true for
higher values (γ >1)of input levels.
 C=gamma=1 means it is an identity transformations.
 Variety of devices used for image capture , printing, and display respond
according to a power law.
 Process used to correct these power law response phenomena is called
gamma correction.

16. Some Basic Intensity Transformation
Functions

17. Some Basic Intensity Transformation
Functions

18. Power Law (Gamma) Transformations
 Images that are not corrected properly look either bleached out or too dark.
 Varying gamma changes not only intensity, but also the ratio of red to green to blue in
a color images.
 Gamma correction has become increasingly important, as the use of the digital images
over internet.
 Useful for general purpose contrast manipulation.
 Apply gamma correction on CRT (Television, monitor), printers, scanners etc.
 Gamma value depends on device.

19. Some Basic Intensity Transformation
Functions