What is Image Enhancement? | Image Processing #3
What is Image Enhancement?
Image enhancement is the process of making images more useful (such as making images more visually appealing, bringing out specific features, removing noise from images and highlighting interesting details in images).
Spatial and Frequency Domains
- Spatial domain techniques manipuletes the pixels of an image directly. This process happens in the image’s coordinate system, also known as the spatial domain.
- Frequency domain techniques transforms an image from the spatial domain to the frequency domain. In this process, Mathematical transformations (such as the Fourier transform) are used. The image can be modified by manipulating its frequency components.
Note: In this blog post, only techniques that operate in spatial domain will be explained. Also, grey levels are assumed to be given in the range [0.0, 1.0].
Basic Spatial Domain Image Enhancement
Most spatial domain enhancement operations can be reduced to the form 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 some operator defined over some neighbourhood of (x, y).
Point Processing Techniques
When the neighborhood is pixel itself, simplest spatial domain operations occur. Point processing operation take the form s = T(r) where s refers to the processed image pixel value and r refers to the original image pixel value.
1. Negative Images
- Negative images are useful for enhancing details.
- s = intensity_max — r
2. Thresholding
- Thresholding transformations are useful for segmentation in which we want to isolate an object of interest from a background.
- If thresholding is too low, image contains higher intensity values more.
- If thresholding is too high, image contains lower intensity values more.
3. Some of The Grey Level Transformations
Logarithmic Transformations
- The general form of the log transformation is s = c * log(1 + r)
- The log transformation maps a narrow range of low input grey level values into a wider range of output values.
- The inverse log transformation performs the opposite transformation.
- Log functions are particularly useful when the input grey level values may have an extremely large range of values
- C is generally set to 1.
- Grey levels must be in the range [0.0, 1.0].
Power Law (Gamma Correction) Transformations
- Power law transformations have the following form:
- Map a narrow range of dark input values into a wider range of output values or vice versa.
- Varying γ gives a whole family of curves.
- C is generally set to 1.
- Grey levels must be in the range [0.0, 1.0].
Gamma Correction
- Display devices do not respond linearly to different intensities.
Piecewise Linear Transformation Functions
Gray Level Slicing
- Highlights a specific range of grey levels, other levels can be suppressed or maintained.
Bit Plane Slicing
- By isolating particular bits of the pixel values in an image we can highlight interesting aspects of that image.
- Higher-order bits usually contain most of the significant visual information.
- Lower-order bits contain subtle details.
Image Enhancement
The histogram of an image shows us the distribution of grey levels in the image. Useful in image processing, especially in segmentation and enhancement.
The high contrast image has the most evenly spaced histogram.
Histogram Equalisation
- Spreading out the frequencies in an image (or equalising the image) is a simple way to improve dark or washed out images.
Equalization Transformation Function
Histogram Matching
- There are applications in which histogram equalization is not suitable.
- It is useful sometimes to be able to specify the shape of the histogram that we wish the processed image to have.
References
- “Digital Image Processing”, Rafael C. Gonzalez & Richard E. Woods, Pearson/Prencite Hall, 2018.
- “AIN430 Fundamentals of Image Processing”, Ali Seydi Keçeli
