Applications of Deep Learning in Improving Graphics

Artificial intelligence and machine learning can train a robot to do anything you want to. One such subset of machine learning that enables you to do that is deep learning. It uses artificial neural networks to train the robot or software. With the help of deep learning, the robot is able to recognize speech, language and objects to make decisions based on them.

Let’s see how the computer graphics industry has been using it for some very interesting improvements in images.

Colouring Grayscale Images

Zhang et al used deep learning to colorize grayscale images. It involves feeding the black and white images to the Lab color space. This colour space calculates the probability of colours based on the intensity of lightness. It predicts colours based on how humans see them. Hence, its colourization proves to be quite accurate.

Improving Image Quality – A Deep Learning Application by Intel

Intel used deep learning for converting 3D-rendered images into photorealistic images. It is used for gaming and they first tested it with Grand Auto Theft 5. The network they developed collects mathematical information on the colour, texture, depth and other properties of the rendered image directly from the game engine. It uses it to make corrections in the image to give you a far more realistic environment.


One of the most recent developments in using deep learning has been to convert images into an animated GIF. Developed by researchers at the University of Washington, it focuses on providing motion to a still image based on prediction of its movement. 

They started training the model for developing motion in a waterfall. To do so, they used video clips of water bodies to make it understand the motion of a fluid. Based on that, it is able to predict the motion for a still image and move its pixels to create motion. To avoid any distortions, they used a technique called symmetric splatting. Here, the frame gets looped and you get a real video.

Deep learning has so many applications and possibilities. It can make it difficult for you to predict which image is real and which one is edited. Distinguishing between the real and virtual space is also getting tougher now. What more possibilities do you think of in this field?

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