Color Restoration Method of Art Images Based on Perceptual Network and Oriented to Serial Data Application in Wireless Systems

Introduction

Image restoration technology has evolved significantly in recent years, with early research focusing on algorithms based on variational differential equations and texture synthesis. While these methods are effective for small areas of image defects, they struggle with larger areas of damage. Traditional image restoration techniques rely on mathematical models and algorithms, such as denoising, deblurring, completion, and enhancement, often based on assumed noise models and methods like inverse filtering, Wiener filtering, and wavelet transforms (Wali et al., 2023). However, these approaches are limited by the accuracy of noise models and manual parameter settings, making them less effective for complex or unknown noise and distortion scenarios. In contrast, artificial neural network-based restoration algorithms offer greater adaptability and robustness by training deep neural networks to automatically learn image features and restoration rules, leading to more accurate results. Recent advancements in deep learning-based image restoration have introduced attention mechanisms, multi-scale feature fusion strategies, and adversarial training in generative adversarial networks (GANs) to improve global semantic information capture, local and global feature integration, and the realism of restored images. For instance, context encoding-based methods combine encoder-decoder networks with discriminative networks to learn contextual features and generate semantically reasonable missing regions (Yu, W et al., 2024). Other approaches, such as global and local consistency-based discriminative networks and rough to fine architectures further enhance image restoration by maintaining boundary consistency and leveraging spatial correlations.

Despite these advancements, challenges remain, particularly in handling large-area defects and ensuring the structural integrity of restored images. Image restoration methods often refer to missing information as “holes” or “masks,” which are represented as binary masks. These methods are crucial for applications like old photo restoration, text removal, and privacy protection in image transmission. The use of GANs for image restoration has gained increasing attention due to their ability to synthesize whole image information, improving both efficiency and the quality of restoration results. In the context of wireless systems, image restoration technology plays a vital role in addressing the limitations of wireless sensor networks, particularly in energy-constrained environments. As wireless sensors become smaller and more integrated into various applications, such as environmental monitoring, healthcare, and smart cities, the need for efficient image restoration methods that can operate under limited energy resources becomes increasingly important (Jamshed et al., 2022). This paper proposes a perception network-based method for art image color restoration, leveraging sequence data applications in wireless systems to enhance the accuracy and efficiency of image restoration in resource-constrained scenarios. Through this research, we aim to provide a new approach and technological means for the field of image restoration, contributing to the advancement and application of related technologies.