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Image Processing 101 Chapter 2.1: Image Enhancement
Image enhancement refers to the process of highlighting certain information of an image, as well as weakening or removing any unnecessary information according to specific needs. For example, eliminating noise, revealing blurred details, and adjusting levels to highlight features of an image.
Image enhancement techniques can be divided into two broad categories:
- Spatial domain — enhancement of the image space that divides an image into uniform pixels according to the spatial coordinates with a particular resolution. The spatial domain methods perform operations on pixels directly.
- Frequency domain — enhancement obtained by applying the Fourier Transform to the spatial domain. In the frequency domain, pixels are operated in groups as well as indirectly.
This article discusses the image enhancement techniques implemented in the spatial domain.
Types of Spatial Domain Technique
Types of spatial domain operator:
- Point operation (intensity transformation) - Point operations refer to running the same conversion operation for each pixel in a grayscale image. The transformation is based on the original pixel and is independent of its location or neighboring pixels. Read more about Point operation »
- Spatial filter (or mask, kernel) - The output value depends on the values of f(x,y) and its neighborhood. Read more about Spatial filter »
Applications of image enhancement
Here are some examples of image enhancement:
- Deblur images
- Contrast adjustment
- Brighten an image
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Smooth and sharpen
Image smoothing is a digital image processing technique that reduces and suppresses image noises. Commonly seen smoothing filters include average smoothing, Gaussian smoothing, and adaptive smoothing. Read more about smoothing filters
Image sharpening filters highlight edges by removing blur. Read more about sharpening filters
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Noise removal
Noises are introduced to images at the point of capture from cameras, printing, or during transmission. In terms of image processing, noises can be identified with a variance of intensity from its neighbor pixels. There are various types of noises. For example, Gaussian noise changes each pixel by a (usually) small amount, and salt-and-pepper noise (impulse noise) randomly scatters white or black pixels over the image. Noise removal techniques reduce the visibility of noises by smoothing the image using linear or non-linear filters. [1]
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Grayscale image histogram equalization
Histogram equalization refers to the transformation where an output image has approximately the same number of pixels at each gray level, i.e., the histogram of the output is uniformly distributed. Read more about grayscale image histogram equalization
References: [1] https://www.ijsr.net/archive/v6i3/25031706.pdf
This article is Part 4 in a 7-Part Series.
- Part 1 - Image Processing 101 Chapter 1.1: What is an Image?
- Part 2 - Image Processing 101 Chapter 1.2: Color Models
- Part 3 - Image Processing 101 Chapter 1.3: Color Space Conversion
- Part 4 - Image Processing 101 Chapter 2.1: Image Enhancement
- Part 5 - Image Processing 101 Chapter 2.2: Point Operations
- Part 6 - Image Processing 101 Chapter 2.3: Spatial Filters (Convolution)
- Part 7 - Morphological Operations