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Color Transfer Algorithm between Images Based on a Two-Stage Convolutional Neural Network.

Min Xu¹, Youdong Ding¹

¹Shanghai Film Academy, Shanghai University, Shanghai 200072, China.

Sensors (Basel, Switzerland)

|October 27, 2022

Summary

This summary is machine-generated.

This study introduces a novel two-stage convolutional neural network (CNN) for image color transfer. The algorithm enhances visual effects and emotional coloring, outperforming existing methods in complex scenes.

Keywords:

CNN VGG19 color transfer emotional color reference image

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Area of Science:

Computer Vision
Artificial Intelligence
Image Processing

Background:

Accurate and aesthetically pleasing color transfer between images remains a challenge.
Existing colorization methods often struggle with complex scenes and emotional enhancement.

Purpose of the Study:

To propose a novel two-stage convolutional neural network (CNN) algorithm for advanced image color transfer.
To enhance the emotional coloring of images using a palette-based approach.
To improve upon existing image colorization techniques.

Main Methods:

Utilized a VGG19-based network for the first stage (Reference Image-based Color Transfer - RICT) to extract image features.
Employed a Progressive Convolutional Neural Network (PCNN) for the second stage (Palette-based Emotional Color Enhancement - PECE).
Compared reference image palettes, emotional values, and color proportions for enhancement.

Main Results:

The proposed algorithm demonstrated superior visual effects compared to several main colorization methods.
Both subjective evaluations and objective data confirmed the model's effectiveness.
The algorithm proved effective across various complex image scenes.

Conclusions:

The developed two-stage CNN algorithm offers a significant improvement in image color transfer and emotional enhancement.
The method shows promise for applications in digital restoration, medical imaging, art restoration, and remote sensing.