Everything You Need to Know about Deep Learning for Digital Image Processing

What is Deep Learning? 

Deep learning is an artificial intelligence (AI) function that imitates the workings of the human brain in processing data and creating patterns for use in decision making. It is also known as deep neural learning or deep neural network. 

What is Deep Learning for Digital Image Processing?  

Computers today can not only automatically classify photos they can also describe the various elements in pictures and write short sentences describing each segment with proper English and grammar. Deep Learning is used in the domain of digital image processing to solve difficult problems such as image colorization, classification, segmentation and detection. Deep Learning methods such as Convolutional Neural Networks (CNNs) mostly improve prediction performance using big data and plentiful computing resources and have pushed the boundaries of what was previously possible.  

Why is Deep Learning beneficial in Digital Image Processing? 

Deep Learning models, with their multi-level structures, are very helpful in extracting complicated information from input images. Learning-based algorithms, especially machine learning, has become the state-of- the- art approach in image processing, because they solve problems based on the data, not engineered algorithms. DL models are able to extract very complicated information from input images. Convolutional Neural Networks are able to take the advantage of Graphics Processing Unit (GPU) for computation while many other network architectures cannot. That is why CNN has become the most successful sub-domain in Deep Learning and most DL researchers, especially in image processing and computer vision are actually working on Deep CNN. 

Challenges with using Deep Learning 

There are also challenges introduced by Deep Learning. The latest DL approaches may achieve substantially better accuracy but this jump comes at the cost of billions of additional math operations and an increased requirement for processing power. DL requires these computing resources for training and to a lesser extent for inference. It is essential to have dedicated hardware for training and AI accelerated platforms for inference for developers of AI.  

Training a DNN takes a very long time. Depending on computing hardware availability, training can take a matter of hours or days. Moreover, training for any given application often requires many iterations as it entails trial and error with different training parameters.  

Currently, deep learning is already being utilized in Google and image searches. It allows you to search several image – terms like ‘dance.’ It’s also used to get you Smart replies to your Gmail and will soon be used in machine translation. 

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