Study on the Application of Visual Communication Design in APP Interface Design in the Context of Deep Learning

Area of Science:

• Visual Communication Design
• Computer Vision
• Artificial Intelligence

Background:

• Visual communication design integrates linguistics and semiotics, fostering innovation.
• Preserving cultural heritage, like historical images, is crucial for diversity.
• Deep learning (DL) and computer vision (CV) enable automatic grayscale image colorization.

Purpose of the Study:

• To introduce an enhanced deep learning-based automated historical image colorization (EDL-AHIC) technique.
• To improve the conversion of grayscale historical images into color images using DL.
• To apply EDL-AHIC in wireless network-enabled visual communication contexts.

Main Methods:

• The EDL-AHIC technique extracts both local and global features from images.
• An enhanced capsule network (ECN) model is utilized for global feature extraction.
• A fusion layer and decoding unit generate the output chrominance component.

Main Results:

• The EDL-AHIC technique effectively converts grayscale images to color.
• Experimental validation confirms the technique's effectiveness.
• Comparative studies demonstrate the superiority of EDL-AHIC over existing methods.

Conclusions:

• The proposed EDL-AHIC technique offers an effective solution for automated historical image colorization.
• This method contributes to the preservation of cultural heritage through visual communication.
• The technique shows significant advancements in DL-based image processing for visual communication.