Jian Wang , Mengyu Luo , Xinlei Chen , Heming Xu , Junseok Kim
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引用次数: 0
Abstract
With the increasing popularity of digital images, developing advanced algorithms that can accurately reconstruct damaged images while maintaining high visual quality is crucial. Traditional image restoration algorithms often struggle with complex structures and details, while recent deep learning methods, though effective, face significant challenges related to high data dependency and computational costs. To resolve these challenges, we propose a novel image inpainting model, which is based on a modified Lengyel–Epstein (LE) model. We discretize the modified LE model by using an explicit Euler algorithm. A series of restoration experiments are conducted on various image types, including binary images, grayscale images, index images, and color images. The experimental results demonstrate the effectiveness and robustness of the method, and even under complex conditions of noise interference and local damage, the proposed method can exhibit excellent repair performance. To quantify the fidelity of these restored images, we use the peak signal-to-noise ratio (PSNR), a widely accepted metric in image processing. The calculation results further demonstrate the applicability of our model across different image types. Moreover, by evaluating CPU time, our method can achieve ideal repair results within a remarkably brief duration. The proposed method validates significant potential for real-world applications in diverse domains of image restoration and enhancement.
随着数字图像的日益普及,开发既能准确重建受损图像又能保持高视觉质量的先进算法至关重要。传统的图像修复算法往往难以处理复杂的结构和细节,而最新的深度学习方法虽然有效,却面临着与高数据依赖性和计算成本相关的重大挑战。为了解决这些难题,我们提出了一种基于改进的伦盖尔-爱泼斯坦(Lengyel-Epstein,LE)模型的新型图像内绘模型。我们使用显式欧拉算法对修改后的 LE 模型进行离散化。我们在二值图像、灰度图像、索引图像和彩色图像等各种类型的图像上进行了一系列修复实验。实验结果证明了该方法的有效性和鲁棒性,即使在噪声干扰和局部损伤的复杂条件下,所提出的方法也能表现出优异的修复性能。为了量化这些修复图像的保真度,我们使用了峰值信噪比 (PSNR),这是一个在图像处理中被广泛接受的指标。计算结果进一步证明了我们的模型适用于不同类型的图像。此外,通过评估 CPU 时间,我们的方法可以在极短的时间内实现理想的修复效果。所提出的方法验证了在图像修复和增强等不同领域的实际应用中的巨大潜力。
期刊介绍:
The central focus of this journal is the computer analysis of pictorial information. Computer Vision and Image Understanding publishes papers covering all aspects of image analysis from the low-level, iconic processes of early vision to the high-level, symbolic processes of recognition and interpretation. A wide range of topics in the image understanding area is covered, including papers offering insights that differ from predominant views.
Research Areas Include:
• Theory
• Early vision
• Data structures and representations
• Shape
• Range
• Motion
• Matching and recognition
• Architecture and languages
• Vision systems