Abstract:
Traditionally or Manually grey scale image colorization requires a lot of time and
engage user to consider very much minor details in the form of positioning various
colour scribbles, performing image segmentation or visualizing at similar pictures
even effective software like photoshop can take about month to colorize a black and
white image. This is all because of the vast range of shades present in the picture.
Colour of the pixel are highly dependent on the features of its neighbours
convolutional neural networks. By working on a dataset of different objects like sky,
man, cats, dogs, bulls etc.Solving the problem by converting them into black and
white 256x256x1 arrays which contain only the light values of the images in the Lab
colour schema our model output 256x256x3 arrays contain blue-yellow, green-red
and white values .after going through several CNN having different convolutional and
training size we tried to build a more advance model that is comprise of the Vggl6
architecture and autoencoders which give us more ppossible results.
