Image processing is a process that is used to perform operations like filtering, and transforming on the image. That is the simplest definition of image processing. These operations are used to retrieve useful information from images. It is a form of signal process. Image-processing has a lot of advantages. These advantages expands into many areas like engineering, medical science, agriculture, industry and military.
What is an image?
An image is created of pixels. Pixel is the smallest component of the image. Each pixel stores a value proportional to the light intensity. One image includes millions of pixels. I have illustrated how pixels are prepared in the below image. You can see pixels of images using Photoshop.
An image has two-dimension(2D) signal it can represent as f(x,y). At any point of the image the value of f(x, y) is assigned to the corresponding pixel. Also, we can represent an image as a two-dimensional matrix or array. Below are some of the major fields where digital image processing is commonly used.
- Remote sensing
- Medical Imaging
- Automated inspection
- Atmospheric study
- Movement detection
First, you want to choose an image in your computer right click->properties and select details tab. The image I picked includes 589,440 pixels. The colour image (RGB) includes 3 bytes per pixel.
1byte = 8bits
1pixel = 1byte
8(bit) x 3 = 24(Bit depth)
589,440pixels = 589,440bytes
589,440 x 3 (3 byte include RGB colour image) = 1,768,320byte
size = 1.686 MB
Types of image processing
Analog image processing
Analog signals are used to perform analog image processing. Analog signal consists of a two-dimensional analog signal. ex: The human voice
Digital image processing
Digital image processing uses digital signals to perform the related operations. In here we will discuss digital image processing techniques, usages and the important features of digital image processing.
Three Types of images
- Binary Image – these images includes only two colours black and white
- Colour image – colour image includes three colours: red, green and blue (RGB)
- Grey-scale image – greyscale image has no colour and only shades of grey
Steps in digital image processing
Image acquisition is the first step of digital image processing. Image acquisition is used to digitize the signals and create a digital image.
Preprocessing can be defined as operations. There are many operations: filtering, contrast enchantment, enhance image quality, edge detection, shape refinement, noise elimination, etc…
Segmentation is the third fundamental step in digital image processing. Partitioning of the image into small segments is the main process of the segmentation. Segments are a set of pixels or image objects.
4.Feature selection (Description)
Extracts details (description) of image objects suitable for further processing by computers.
5.Recognition and Interpretation
To add a label to the object based on the descriptors details. Interpretation assigns a sense to the collection of labelled objects.
knowledge base supports in the efficient collection and inter-module cooperation.
Analog signal conversion
There are two major methods to convert the analog signals into digital signals. Those two methods are used to digitize the image.
- Sampling – Image representation with 2D finite matrix. This converts analog signal into the discrete-time signal, a sequence of real numbers.
- Quantization – this method replaces each real number with a finite set of discrete values. Each matrix element shows one of the finite set of discrete value.
Image enhancement is increasing the quality of the digital image. There are so many image enhancement techniques. The following methods are frequently used for image processing.
- Contrast stretching
This is a basic image enhancement technique that aims to increase the contrast of the image. Stretching the range of intensity value is the concept of this technique.
- Median Filtering
This is a digital filtering technique which is used to remove noise from the images.
- Histogram equalization
This improves contrast and can be applied on an entire image or on only part of an image.
- Adaptive histogram equalization
Adaptive histogram equalization use neighbouring region of the image that compares with other pixels and provides an enhanced output image.
- Decorrelation stretch
This method finds separation between colours and the overstated colours boosts visual perception.
- Mask filtering
These filters are used to edge detection, blurring and noise reduction of the images.
I hope this information will help you to get a basic understanding of image processing. Thank you.