Automated Image Colorization using Autoencoders

Abstract

Image colorization, the process of infusing grayscale images with vibrant hues to heighten their visual allure and perceptual significance, has undergone a transformative evolution. Traditionally, manual techniques employing software such as Photoshop dominated this artistic endeavour. However, a paradigm shift has occurred with the advent of deep learning architectures, particularly the autoencoder, simplifying and revolutionizing the colorization process. Autoencoders, a subset of Convolutional Neural Networks (CNNs), have emerged as the backbone of automatic picture colorization. In the realm of automatic colorization, the intricate dance between encoder and decoder within the autoencoder framework takes centre stage. The encoder, employing a sequence of CNNs and down-sampling techniques, distils the essential features of the input image into a reduced-dimensional representation. This encoded information becomes the key to unlocking the color palette for the subsequent reconstruction process. The decoder, equipped with CNNs and up-sampling, meticulously endeavours to recreate the original image based on the learned features. This symbiotic relationship between encoder and decoder within autoencoders exemplifies the power of deep learning in transforming grayscale images into vivid, chromatic masterpieces. As technology continues to advance, the synergy of manual and automated colorization techniques promises a future where every image is a canvas waiting to be painted with the strokes of artificial intelligence.