Image Colorization By Capsule Networks
G\"okhan \"Ozbulak

TL;DR
This paper explores a modified Capsule Network, called ColorCapsNet, for image colorization, leveraging deep features and a new loss function to improve colorization performance.
Contribution
It introduces a novel CapsNet-based architecture for image colorization, integrating deep features and MSE loss, which is a new application of CapsNets.
Findings
Promising results on DIV2K dataset
Effective adaptation of CapsNet for colorization
Potential for further research in capsule-based image processing
Abstract
In this paper, a simple topology of Capsule Network (CapsNet) is investigated for the problem of image colorization. The generative and segmentation capabilities of the original CapsNet topology, which is proposed for image classification problem, is leveraged for the colorization of the images by modifying the network as follows:1) The original CapsNet model is adapted to map the grayscale input to the output in the CIE Lab colorspace, 2) The feature detector part of the model is updated by using deeper feature layers inherited from VGG-19 pre-trained model with weights in order to transfer low-level image representation capability to this model, 3) The margin loss function is modified as Mean Squared Error (MSE) loss to minimize the image-to-imagemapping. The resulting CapsNet model is named as Colorizer Capsule Network (ColorCapsNet).The performance of the ColorCapsNet is evaluated…
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Taxonomy
TopicsImage Enhancement Techniques · Generative Adversarial Networks and Image Synthesis · Advanced Image Processing Techniques
MethodsVisual Geometry Group 19 Layer CNN · Capsule Network · Colorization
