College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China.
Technology School, Jilin Business and Technology College, Changchun, China.
PLoS One. 2024 Sep 6;19(9):e0306706. doi: 10.1371/journal.pone.0306706. eCollection 2024.
In the field of image processing, common noise types include Gaussian noise, salt and pepper noise, speckle noise, uniform noise and pulse noise. Different types of noise require different denoising algorithms and techniques to maintain image quality and fidelity. Traditional image denoising methods not only remove image noise, but also result in the detail loss in the image. It cannot guarantee the clean removal of noise information while preserving the true signal of the image. To address the aforementioned issues, an image denoising method combining an improved threshold function and wavelet transform is proposed in the experiment. Unlike traditional threshold functions, the improved threshold function is a continuous function that can avoid the pseudo Gibbs effect after image denoising and improve image quality. During the process, the output image of the finite ridge wave transform is first combined with the wavelet transform to improve the denoising performance. Then, an improved threshold function is introduced to enhance the quality of the reconstructed image. In addition, to evaluate the performance of different algorithms, different densities of Gaussian noise are added to Lena images of black, white, and color in the experiment. The results showed that when adding 0.010.01 variance Gaussian noise to black and white images, the peak signal-to-noise ratio of the research method increased by 2.58dB in a positive direction. The mean square error decreased by 0.10dB. When using the algorithm for denoising, the research method had a minimum denoising time of only 13ms, which saved 9ms and 3ms compared to the hard threshold algorithm (Hard TA) and soft threshold algorithm (Soft TA), respectively. The research method exhibited higher stability, with an average similarity error fluctuating within 0.89%. The above results indicate that the research method has smaller errors and better system stability in image denoising. It can be applied in the field of digital image denoising, which can effectively promote the positive development of image denoising technology to a certain extent.
在图像处理领域，常见的噪声类型包括高斯噪声、椒盐噪声、斑点噪声、均匀噪声和脉冲噪声。不同类型的噪声需要不同的去噪算法和技术来保持图像质量和保真度。传统的图像去噪方法不仅去除了图像噪声，还导致图像细节丢失。它不能保证在去除噪声信息的同时，保留图像的真实信号。针对上述问题，实验中提出了一种结合改进阈值函数和小波变换的图像去噪方法。与传统的阈值函数不同，改进的阈值函数是一个连续函数，可以避免图像去噪后的伪吉布斯效应，提高图像质量。在这个过程中，首先将有限脊波变换的输出图像与小波变换相结合，以提高去噪性能。然后，引入改进的阈值函数来增强重建图像的质量。此外，为了评估不同算法的性能，实验中在 Lena 黑白图像和彩色图像上分别添加了不同密度的高斯噪声。结果表明，当向黑白图像添加方差为 0.01 的高斯噪声时，研究方法的峰值信噪比正向增加 2.58dB，均方误差降低 0.10dB。在使用算法进行去噪时，研究方法的最小去噪时间仅为 13ms，分别比硬阈值算法（Hard TA）和软阈值算法（Soft TA）节省 9ms 和 3ms。研究方法具有更高的稳定性，平均相似度误差波动在 0.89%以内。上述结果表明，该研究方法在图像去噪中具有较小的误差和更好的系统稳定性。它可以应用于数字图像去噪领域，在一定程度上可以有效地促进图像去噪技术的积极发展。
