AMNEDP Laboratory, Department of Analysis, University of Sciences and Technology Houari Boumediene, Algiers 16111, Algeria.
UMR 1253, iBrain, INSERM, Université de Tours, 37000 Tours, France.
Sensors (Basel). 2022 Mar 11;22(6):2199. doi: 10.3390/s22062199.
In remote sensing applications and medical imaging, one of the key points is the acquisition, real-time preprocessing and storage of information. Due to the large amount of information present in the form of images or videos, compression of these data is necessary. Compressed sensing is an efficient technique to meet this challenge. It consists in acquiring a signal, assuming that it can have a sparse representation, by using a minimum number of nonadaptive linear measurements. After this compressed sensing process, a reconstruction of the original signal must be performed at the receiver. Reconstruction techniques are often unable to preserve the texture of the image and tend to smooth out its details. To overcome this problem, we propose, in this work, a compressed sensing reconstruction method that combines the total variation regularization and the non-local self-similarity constraint. The optimization of this method is performed by using an augmented Lagrangian that avoids the difficult problem of nonlinearity and nondifferentiability of the regularization terms. The proposed algorithm, called denoising-compressed sensing by regularization (DCSR) terms, will not only perform image reconstruction but also denoising. To evaluate the performance of the proposed algorithm, we compare its performance with state-of-the-art methods, such as Nesterov's algorithm, group-based sparse representation and wavelet-based methods, in terms of denoising and preservation of edges, texture and image details, as well as from the point of view of computational complexity. Our approach permits a gain up to 25% in terms of denoising efficiency and visual quality using two metrics: peak signal-to-noise ratio (PSNR) and structural similarity (SSIM).
在遥感应用和医学成像中，关键点之一是信息的获取、实时预处理和存储。由于信息以图像或视频的形式存在，因此需要对这些数据进行压缩。压缩感知是应对这一挑战的有效技术。它包括通过使用最少数量的非自适应线性测量来获取信号，假设该信号可以具有稀疏表示。在完成这个压缩感知过程后，必须在接收器端执行原始信号的重建。重建技术通常无法保留图像的纹理，并且倾向于平滑其细节。为了解决这个问题，我们在这项工作中提出了一种结合全变差正则化和非局部自相似性约束的压缩感知重建方法。通过使用避免正则化项的非线性和不可微性的增广拉格朗日方法来优化该方法。所提出的算法，称为基于正则化项的去噪压缩感知(DCSR)，不仅可以进行图像重建，还可以进行去噪。为了评估所提出算法的性能，我们将其性能与最先进的方法（如 Nesterov 算法、基于分组的稀疏表示和基于小波的方法）进行比较，从去噪、边缘、纹理和图像细节的保留以及计算复杂度的角度进行比较。我们的方法使用两种度量标准（峰值信噪比 (PSNR) 和结构相似性 (SSIM)），在去噪效率和视觉质量方面可以提高 25%。
