Multipurpose image colorization: a novel pipeline using convolutional neural networks

Abstract

The colorization of monochromatic images has demonstrated utility in enhancing human comprehension of images and boosting the accuracy of succeeding image-processing tasks. Nonetheless, current fully automated colorization methodologies often exhibit optimal performance based on the input image’s nature and the employed algorithms’ architectural specifics. In response to this challenge, this paper introduces a novel methodology aimed at effectively predicting the most suitable colorization model for a given input image. This comprehensive approach is characterized by exceptional accuracy across diverse datasets.