使用冲浪算法修复数字图像

Shanmukhaprasanthi Tammineni, Swaraiya Madhuri Rayavarapu, S. Gottapu, Raj Kumar Goswami
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引用次数: 0

摘要

在当代,科学和创造性工作的保存往往有赖于对胶片和图像档案的利用,因此强调了图像处理作为一项关键工作的重要性。图像上色是指对图像进行数字修改,使不了解原始图像的观众无法察觉调整的过程。图像内绘是一种技术,主要用于利用从相关图像的匹配特征中获取的信息来恢复图像中的受损区域。这一过程包括填充受损区域和去除不需要的对象。我们所考虑的 SURF(加速鲁棒特征)算法分为三个主要阶段。首先,确定受损图像和相关图像的基本特征。第二阶段,确定受损图像与相关图像之间的平移、缩放和旋转关系。最后,通过应用反变换重建受损区域。可以使用结构相似性指数(SSIM)、峰值信噪比(PSNR)和平均平方误差(MSE)等指标来评估涂色图像的质量。实验结果证明,建议的涂色技术在速度和质量方面都很有效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
DIGITAL IMAGE RESTORATION USING SURF ALGORITHM
In contemporary times, the preservation of scientific and creative endeavours often relies on the utilization of film and image archives, hence emphasizing the significance of image processing as a critical undertaking. Image inpainting refers to the process of digitally altering an image in a manner that renders the adjustments imperceptible to a viewer lacking knowledge of the original image. Image inpainting is a technique mostly employed to restore damaged regions within an image by utilizing information obtained from matching characteristics in relevant images. This process involves filling in the damaged areas and removing undesired objects. The SURF (Speeded Up Robust Feature) algorithm under consideration is partitioned into three primary phases. Firstly, the essential characteristics of the impaired image and the pertinent image are identified. In the second stage, the relationship between the damaged image and the relevant image is determined in terms of translation, scaling, and rotation. Ultimately, the destroyed area is reconstructed through the application of the inverse transformation. The quality assessment of inpainted images can be evaluated using metrics such as Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR), and Mean Squared Error (MSE). The experimental findings provide evidence that the suggested inpainting technique is effective in terms of both speed and quality.
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