Artificial Intelligence is Key Role for Solving Image Processing Tasks

• By Aniya More
• 16-02-2022
• Artificial Intelligence

Did you know that, like the human brain, you can also teach machines and androids to recognize and interpret images? That's right. Due to the advancement of technology, we have been able to implement tools and intelligence into these systems to conduct image processing on a broader level. And it has helped us to merge AI with our innovation and create an image processing system that can analyze various kinds of photos and prints in a more thorough way than our vision and brain.

In today's world, where information is slowly spreading virtually, image processing has become a promising method in different fields, whether in business, security, or entertainment. You can now see its implementations in face recognition systems for public security systems, various puzzle games at breakout escape rooms in Bangalore, or object detection and video identification in research labs. There is endless scope for image processing tasks in several domains. And to unravel their possibilities, AI can play a significant role here.

So, let us look at some key aspects where AI has collaborated with image processing to aid in its advancement and enhance the overall outcome:

Visualization:

AI has helped implement picture visualization that enables us to highlight and focus on those aspects of the image which is otherwise not possible in the file formats of JPEG or JPG. We can accomplish it by converting the file into an SVG vector format.

Pattern Recognition:

What differentiates the SVG files from JPG and JPEG files is the feature of accommodating machine learning in the SVG image format. And it makes various elements like object identification and location, sharpness measurement in a picture, and pattern recognition easier, thereby giving us in-depth details of the image that is not possible in the basic JPG formats.

Object Measurement:

Using AI, we can measure and recognize the unusually placed objects in an image and separate them on a different pixel for a clear printout. And we can also sharpen the blurred aspects of a picture and its objects by improving its clarity and enhancing the overall content quality.

Apart from these factors, we have briefly discussed below some AI methods that are very effective for processing image tasks and creating high-quality and elegant images for our real-life needs:

Neural Networking:

We can use Neural Networking to solve various tasks related to image processing. Some of these include deriving the exact color and sharpness of the image, generating the depth of a particular object in the picture, focusing on minute detail, finding out if there's a criterion that the picture belongs to, detecting any unusual item placement, etc.

Neural networks also contain a set of variations like Convolutional Neural Networks (CNN) and Fully Conventional Neural Networks (F-CNN) that can further divide the various objects placed in an image into different pixels or separate certain items from the image and enhance its quality for additional identification.

U-Net:

Another type of neural network, U-Net, is a faster and heavy-duty working system that can support other grid and network systems in image processing and segmentation. Its upsampling part helps us get clearer, diversified, and sharp image processing results in more than one system that is crucial for research purposes in several sectors. Its precision and high-speed performance make it more reliable than its counterparts.

And though it is more applicable in the biomedical field, we can also use its optimum designed algorithm to reverse image search and get high-quality analysis and interpretation of the various segmented objects in an image.

Conclusion

We can use machine learning and deep learning systems with neural networking algorithms to enforce AI with image processing tasks to get images and videos examined according to a specific requirement. It is a very nascent field and receptive to many interpretations and opportunities due to its massive range of applications in various sectors. By utilizing the techniques of AI in image processing, we can handle any task with utmost precision and analyze any picture depending on the nature of the work and field.

And since its implementation is an impressive feat in the image processing department and is showing remarkable performance, we can expect AI to only advance in this area and provide benefitting results in sectors like medicine, criminology, security, or even agriculture and psychology in the future.