Review Neural Networks about Image Transformation Based on IGC Learning Framework with Annotated Information

TL;DR

This paper introduces the IGC learning framework to unify and analyze various image transformation tasks like style transfer and image translation within deep neural networks, providing a comprehensive review and future directions.

Contribution

It proposes a novel IGC learning framework that unifies different image transformation tasks and offers a new perspective for understanding and categorizing related research.

Findings

IGC learning effectively improves image transformation performance.

Unified framework clarifies development trends across tasks.

Experimental results validate the framework's effectiveness.

Abstract

Image transformation, a class of vision and graphics problems whose goal is to learn the mapping between an input image and an output image, develops rapidly in the context of deep neural networks. In Computer Vision (CV), many problems can be regarded as the image transformation task, e.g., semantic segmentation and style transfer. These works have different topics and motivations, making the image transformation task flourishing. Some surveys only review the research on style transfer or image-to-image translation, all of which are just a branch of image transformation. However, none of the surveys summarize those works together in a unified framework to our best knowledge. This paper proposes a novel learning framework including Independent learning, Guided learning, and Cooperative learning, called the IGC learning framework. The image transformation we discuss mainly involves the general image-to-image translation and style transfer about deep neural networks. From the perspective of this framework, we review those subtasks and give a unified interpretation of various scenarios. We categorize related subtasks about the image transformation according to similar development trends. Furthermore, experiments have been performed to verify the effectiveness of IGC learning. Finally, new research directions and open problems are discussed for future research.