Remove Blur Image Using Bi-Directional Akamatsu Transform and Discrete Wavelet Transform

P. Andono, C. A. Sari
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引用次数: 1

Abstract

Purpose: Image is an imitation of everything that can be materialized, and digital images are taken using a machine. Although digital image capture uses machines, digital images are not free from interference. Image restoration is needed to restore the quality of the damaged image.Methods: Bi-directional Akamatsu Transform is proven to have an effective performance in reducing blur in images. Meanwhile, Discrete Wavelet Transform has been widely used in digital image processing research. We had been investigated the image restoration method by combining Bi-directional Akamatsu Transform and Discrete Wavelet Transform. Bi-directional Akamatsu Transform applied in Low-Low (LL) sub-band is the Discrete Wavelet Transform decomposition image most similar to the original image before decomposing. In this study, there are still shortcomings, including the determination of the values of N, up_enh, and down_enh, which are still manual. Manually setting the three values makes the Bi-directional Akamatsu Transform method not get the best results. With the use of machine learning methods can get better restoration results. Further testing is also needed for a more diverse and robust blur. The image data has a resolution of 256x256, 512x512, and 1024x1024. The image will be directly converted to a grey-scale image. The converted image will be given an attack model: average blur, gaussian blur, and motion blur. The image that has been attacked will apply two restoration methods: the proposed method and the Bi-direction Akatamatsu Transform. These two restoration images will then be compared using PSNR.Result: The average PSNR value from the restoration of the proposed method is 0.1446 higher than the average PSNR value from the restoration of the Bi-directional Akamatsu Transform method. When we compare it with the average PSNR value of the Akamatsu Transform restoration method, the average PSNR of the proposed method is 0.2084.Value: The combination of DWT and akamatsu transform results produce good PSNR values even though they have gone through the blurring method in image restoration.
利用双向赤松变换和离散小波变换去除模糊图像
目的:图像是对一切可以物化的东西的模仿,数字图像是用机器拍摄的。虽然数字图像采集使用机器,但数字图像并不是没有干扰的。为了恢复受损图像的质量,需要进行图像恢复。方法:双向赤松变换在消除图像模糊方面具有较好的效果。同时,离散小波变换在数字图像处理研究中得到了广泛的应用。研究了结合双向赤松变换和离散小波变换的图像恢复方法。应用于Low-Low (LL)子带的双向赤松变换是与分解前的原始图像最相似的离散小波变换分解图像。本研究还存在不足,包括N、up_enh、down_enh值的确定,仍然是手工的。手动设置这三个值使得双向赤松变换方法无法获得最佳效果。配合使用机器学习方法可以得到较好的复原效果。进一步的测试也需要更加多样化和健壮的模糊。图像数据的分辨率分别为256x256、512x512和1024x1024。图像将直接转换为灰度图像。变换后的图像将被赋予攻击模型:平均模糊、高斯模糊和运动模糊。对被攻击的图像采用两种恢复方法:本文提出的方法和双向赤松变换。然后使用PSNR对这两个恢复图像进行比较。结果:该方法恢复的平均PSNR值比双向赤松变换方法恢复的平均PSNR值高0.1446。与赤松变换恢复方法的平均PSNR值进行比较,该方法的平均PSNR为0.2084。值:结合DWT和赤松变换的结果,即使在图像恢复中经过模糊处理,也能得到很好的PSNR值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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13
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24 weeks
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