Images have become an integral part of our life and now we have come to a stage where sometimes we don’t even realise we have come across an image. We click, filter, enhance, save, upload and do multiple things on it. We are surrounded by images.

But What is an Image?

An image is the pictorial representation of a form composed in a two-dimensional form. An image is constructed in a multitude of rows and columns, therefore, requires a syntax that can represent an image known as Image Matrix. 

Types of Images

But why all this fuss to build or rebuild an image or perform different functions on it?

All of this is done to extract more information from it or to make an image more useful as per your requirement. This whole process is further, labelled as Image Processing. 

What is Image Processing?

Image Processing is the process of taking an image on a digital platform and performing computer functions on it in order to derive an enhanced version of the same image or to extract the desired information from it. In this process, a raw image becomes the input whereas the output is the same image but is inflated with some characteristics that deliver the usage of it. 

 

Usage of Image Processing

• Instagram & Snapchat filters

• Computer Vision

• Microscopic Imaging

• Facial Recognition

• Object detection

• X-Ray Bone Detection

• Feature Extraction

• Product Design

Phases of Image Processing

• Image Acquisition: This step involves scaling and colour conversion of the given image on a digital platform. 

• Image Enhancement: This step is devoted to making an image aesthetically appealing by identifying and editing the hidden details subjectively. 

• Image Restoration: Like image enhancement, this step also deals with image appeal but this step works on mathematical or probabilistic changes of the image done objectively. 

• Colour Image Processing: This step is dedicated to the colour modelling or processing of the digital image. 

• Wavelets and Multi-Resolution Processing: This step divides the image into multiple degrees for pyramidal representation. 

• Image Comprehension: This step involves data compression for easy and light storage to save the image.  

• Morphological Processing: This step deals with extracting image components via several tools for image representation and shape description.

• Segmentation Procedure: This step undertakes the task of a partition of an image into different proportions or on the basis of object identification. It can be done in two ways: autonomous or rugged segmentation. 

• Representation & Description: This step is related to the output where for the final representation of the image the raw data is transformed into the processed data model. 

• Object Detection & recognition: This step involves labelling of the object where the descriptor provides the necessary information entitled with the image or can move forward to providing information on a particular object of the image. 

Image Processing Toolbox:

Toolbox for Image Processing consists of a brief set of reference-based algorithms and workflow applications of processing, visualising, and analysing an image with the development of an algorithm. It also provides a list of image options or a variety of images that can be operated through image processing for example:

• Crop: The image can be cropped or trimmed by the edges easily to cut out the image you wanted out of the original image.

• Multi-Zone Detection: to be able to formalize and take several documents out of the original single document.

• Curvature Correction: by checking the levels at the right and left side of the image flattening of the pages is curvature correction.

• Lightening Correction: adjusting the image’s brightness and contrast along with the deblurring and reducing the noise in it.

 

Coming back to our toolbox for image processing. Image processing isn't just about the processing of a certain image it diversifies to all the processes an image goes through in its digitalisation procedure. So, let's get started with our toolbox:

Exploring: This substantially deals with acquiring the data of the image through the devices like digital cameras, satellite and airborne sensors, microscopes, telescopes, etc. also it deals with the algorithmic approaches which let you place and manipulate ROIs, including points, line, rectangles, ellipse and freehand shapes. You can segment an image based on various colour spaces. 

Morphological Processing: This process is most fundamentally used by the applications connected with geolocation detections. In this part the noise elimination, contrast adjusting, deblurring and remapping a dynamic range as the multispectral colour composite image takes place. It is the skeletonisation process of a region.

Volume Visualization: Many 3D-specific functions that enable complete image processing workflows with 3D data. But before that, you need to visualise the image, for which you can map the pixel intensity of a 3D volume to opacity to highlight a specific region within the volume. Then finally, using the programmatic functions to use active contours and semantic segmentation to segment the 3D data.

Analysing: Extraction of relevant information from the image, for instance, finding shapes, counting objects or measuring the properties of the object through the image. Its colour-space conversion method precisely deals with finding the line endpoints and very precisely represents the colour independently from the device.

Pros and Cons of Image processing:

   PROs

1. Eliminates the noises and deblurring the digital image.

2. Easy to edit the image density and contrast. 

3. Can be used to shift the formats of the image such a black and white, negative image etc.

4. Great use in medical and radiological devices.

 CONs

1. Their workstations have a high cost which is always set by the software providers.

2. As soon as the system goes down due to any reason, all of the data is gone. 

3. Doesn’t provide a proper storage unit.

Tech Stack 

Python, C++, MATLAB/Octave, R, Objective-C

Platforms

Linux, Windows, Mac OS, Android