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1. #CONVERT BLACK AND WHITE TO COLOR MACHINE LEARNING GENERATOR#
2. #CONVERT BLACK AND WHITE TO COLOR MACHINE LEARNING MANUAL#
3. #CONVERT BLACK AND WHITE TO COLOR MACHINE LEARNING ARCHIVE#

A competition takes place as the generator then tries to get better at producing realistically coloured images, and the discriminator gets better at detecting fake images.

The generator tries to produce realistic (but fake) colours from a black and white image, while the discriminator acts as a judge and tries to identify whether the results are fake or not. Our colourisation GAN is an adversarial algorithm which comprises two AI systems called “generator and discriminator”. These Generative Adversarial Networks (GANs) are better at colouring natural images, leading to more realistic and plausible results and they have become the standard for many image-to-image translation tasks such as generating realistic street scenes from semantic segmentation maps, aerial photography from cartographic maps, and, in our case, image colourisation. Recently, advances within artificial intelligence (AI) have enabled the development of new colourisation algorithms based on deep learning. A successful example is film director Peter Jackson’s critically acclaimed World War I documentary, They Shall Not Grow Old in which modern restoration techniques were used to colourise original footages during the conflict, provided by BBC Archives and the Imperial War Museum (IWM). Since the 1970s however, the technology has improved considerably, resulting in some remarkable video restoration achievements. This includes medical imaging, surveillance systems or restoration of degraded historical images. For this reason, adding colour information to images and improving the quality of colour has become a research area of significant interest for a wide variety of situations that traditionally have resorted to using luminance data alone. It is also essential for understanding the visual world, creating a greater distinction between objects and adding physical variations, such as shadows, reflections or reflectance variations on video frames. However, while the brightness in the image helps us interpret shapes and structures in the image, the perception of colour is very important for modern video viewing.

#CONVERT BLACK AND WHITE TO COLOR MACHINE LEARNING ARCHIVE#

Greyscale content is present in a wide variety of circumstances: from faded “black and white” archive material, to pictures that are intended for analysis by computers ( video tracking or object recognition, for instance) where colour in the picture is discarded to simplify processing. In most cases the decisions on colouring are ambiguous - no rule directly determines a car to be red, blue or yellow without possessing additional knowledge of the scene as it was filmed in real life.

For example, an algorithm could interpret rapid changes in a scene as an area of vegetation, assigning green colours to it, or smooth areas to the sky, inferring blue tones. It has subsequently been shown that the task is complex and results could actually bear little resemblance to the colours in real life due to the large degrees of freedom possible in the task.

#CONVERT BLACK AND WHITE TO COLOR MACHINE LEARNING MANUAL#

Although this improved the efficiency of traditional hand-crafted techniques, it still required a considerable amount of manual effort and artistic experience to achieve acceptable results. The Canadian Wilson Markle introduced a novel computer-assisted technology for adding colour to black and white movies and TV programs in 1970. Colourisation refers to the process of adding colours to greyscale or other monochrome images so that the coloured results are perceptually meaningful and visually appealing.