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Visual System

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Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
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The artistic image processing for visual healing in smart city.

Guangfu Qu¹, Qian Song², Ting Fang³

¹School of Film, Shandong University of Arts, Jinan, 250300, China. qgf1974@163.com.

Scientific Reports

|July 22, 2024

Summary

This study introduces a new deep learning model for processing artistic images to improve mental health in smart cities. The OSViT-BiLSTM model enhances visual healing effects and accurately identifies art, boosting urban quality of life.

Keywords:

Artistic image Deep learning Smart city Visual attention model Visual healing

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Area of Science:

Urban planning and design
Psychology and mental health
Computer science and artificial intelligence

Background:

Smart city (SC) initiatives aim to improve urban living.
Visual healing through art processing is explored for mental well-being.
Current methods for artistic image processing lack efficiency and accuracy.

Purpose of the Study:

To investigate artistic image processing for visual healing in smart cities.
To develop an advanced deep learning model for art image classification and recognition.
To evaluate the impact of processed art images on urban residents' mental health and quality of life.

Main Methods:

Examined the role of artistic image processing in visual healing.
Introduced and improved deep learning technology, specifically the overlapping segmentation vision transformer (OSViT) for image blocks.
Integrated the bidirectional long short-term memory (BiLSTM) algorithm to construct the OSViT-BiLSTM model for artistic image processing and classification.

Main Results:

The OSViT-BiLSTM model achieved 92.9% classification recognition accuracy for art images, exceeding existing algorithms by at least 6.9%.
Over 90% of users reported satisfaction with the visual healing effects of the processed artistic images.
The model demonstrated effectiveness in enhancing image beauty, artistry, and visual healing impact.

Conclusions:

The proposed OSViT-BiLSTM model accurately identifies artistic images and enhances their visual healing properties.
Artistic image processing integrated with deep learning can significantly contribute to urban residents' mental health.
This research offers a valuable reference for implementing visual healing strategies within smart city frameworks.