Image processing (IP) is a way to convert an image to a digital aspect and perform certain functions to get an enhanced image or extract other helpful information from it. The image processing system usually treats all images as 2D signals when applying specific predetermined signal processing methods. Nowadays, image processing is among rapidly growing technologies. It forms a core research area within engineering and computer science disciplines. Image processing has extensive applications in many areas, including astronomy, medicine, industrial robotics, and satellite remote sensing. See also pattern recognition.

• Methods of Image Processing (IP)
• Types of image processing (IP) 
• Fundamental Image Processing Steps
• Applications of Digital Image Processing (DIP)

Methods of Image Processing (IP)

There are two methods for image processing: analog and digital image processing.

• Digital image processing (DIP): digital image processing uses a digital computer to process digital images through an algorithm. Digital image processing is a subcategory or field of digital signal processing. Digital image processing allows the user to take the digital image as an input, perform different algorithms to generate an output, and avoid problems such as building noise and distortion during processing. These algorithms may vary from image to image according to the desired output image. Digital image processing is a particular type of processor used in every electronic device, whether it be CDs, mobile phones, battlefields, satellites, medical and voice detection machines, etc.
• Analog Image Processing (AIP): analog image processing is applied to analog signals and processes only two-dimensional signals. Electrical signals manipulate the images. In analog image processing, analog signals can be periodic or non-periodic. Examples of analog images are television, paintings, medical, etc.

Types of Image Processing (IP)

There are five main types of image processing:

• Visualization: Find objects that are not visible in the image.
• Recognition: Distinguish or detect objects in the picture.
• Sharpening and restoration: Create an enhanced image from the original image.
• Pattern recognition: Measure the various patterns around the objects in the image.
• Retrieval: Browse and search photos from an extensive database of digital images similar to the original image.

Fundamental Image Processing steps

Image Acquisition: Image acquisition is the first step in image processing. This step is also known as preprocessing in image processing. It involves retrieving the image from a source, usually a hardware-based source.

Image Enhancement: Image enhancement is the process of bringing out and highlighting specific features of interest in an image that has been obscured. This can involve changing the brightness, contrast, etc.

Image Restoration: Image restoration is the process of improving the appearance of an image. However, image restoration is done using specific mathematical or probabilistic models, unlike image enhancement. Image Restoration is a function of taking an unethical/noisy image and measuring an unused, new image. Exploitation can occur in many ways, such as action blurring, sound, and camera focus. Image restoration techniques aim to reduce noise and reclaim the loss of decision.

Color Image Processing: Color image processing includes several color modeling techniques in a digital domain. This step has gained prominence due to the effective use of digital images over the internet.

Wavelets and Multiresolution Processing: Wavelets are used to represent images in various degrees of resolution. The images are subdivided into wavelets or smaller regions for data compression and pyramidal representation.

Compression: Compression is a process that reduces the storage required to save an image or the bandwidth necessary to transmit it. This is done particularly when the image is for use on the internet.

Morphological Processing: Morphological processing is a set of processing operations for morphing images based on their shapes.

Segmentation: Segmentation is one of the most challenging steps of image processing. It involves partitioning an image into its constituent parts or objects. 

Representation and Description: After an image is segmented into regions in the segmentation process, each region is represented and described in a form suitable for further computer processing. Representation deals with the image’s characteristics and regional properties. Description deals with extracting quantitative information that helps differentiate one class of objects from the other.

Recognition: Recognition assigns a label to an object based on its description.

Applications of Digital Image Processing (DIP)

• Medical Field
• Remote Sensing
• Machine/Robot vision
• Video processing
• Pattern Recognition