2 research outputs found
A learning-based approach for automatic image and video colorization
In this paper, we present a color transfer algorithm to colorize a broad
range of gray images without any user intervention. The algorithm uses a
machine learning-based approach to automatically colorize grayscale images. The
algorithm uses the superpixel representation of the reference color images to
learn the relationship between different image features and their corresponding
color values. We use this learned information to predict the color value of
each grayscale image superpixel. As compared to processing individual image
pixels, our use of superpixels helps us to achieve a much higher degree of
spatial consistency as well as speeds up the colorization process. The
predicted color values of the gray-scale image superpixels are used to provide
a 'micro-scribble' at the centroid of the superpixels. These color scribbles
are refined by using a voting based approach. To generate the final
colorization result, we use an optimization-based approach to smoothly spread
the color scribble across all pixels within a superpixel. Experimental results
on a broad range of images and the comparison with existing state-of-the-art
colorization methods demonstrate the greater effectiveness of the proposed
algorithm.Comment: Computer Graphics International - 201
Improving Video Generation for Multi-functional Applications
In this paper, we aim to improve the state-of-the-art video generative
adversarial networks (GANs) with a view towards multi-functional applications.
Our improved video GAN model does not separate foreground from background nor
dynamic from static patterns, but learns to generate the entire video clip
conjointly. Our model can thus be trained to generate - and learn from - a
broad set of videos with no restriction. This is achieved by designing a robust
one-stream video generation architecture with an extension of the
state-of-the-art Wasserstein GAN framework that allows for better convergence.
The experimental results show that our improved video GAN model outperforms
state-of-theart video generative models on multiple challenging datasets.
Furthermore, we demonstrate the superiority of our model by successfully
extending it to three challenging problems: video colorization, video
inpainting, and future prediction. To the best of our knowledge, this is the
first work using GANs to colorize and inpaint video clips