Image Denoising Using a Compressive Sensing Approach Based on Regularization Constraints

Abstract

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).

Grade Prediction Modeling in Hybrid Learning Environments for Sustainable Engineering Education

“…Finally, it should be noted that the use of holograms and artificial intelligence will soon be possible via 5G and the future 6G. Faced with the big data-including sounds, words, images, and video-that will have to be taken into account, it will be essential to use efficient methods of data compression, at the level of the acquisition itself, of the compressed sensing type [53,54].…”

Blind Source Separation Based on Double-Mutant Butterfly Optimization Algorithm

“…In this section, N is assumed to be 4, and the pixels of the image are ; is a matrix. Figure 6 illustrates the simulation result and Table 5 compares the similarity coefficient, PI, and duration of separated signals, as well as the SSIM [ 38 ] of the output image. The SSIM proves to be a better error metric for comparing the image quality with better structure preservation.…”

A2TPNet: Alternate Steered Attention and Trapezoidal Pyramid Fusion Network for RGB-D Salient Object Detection

“…Secondly, efficient and simple multi-scale feature integration strategies (e.g., sparse connectivity) may be employed for saliency inference in the future. In addition, the images used that come from datasets are far from what happens in real life [59]. In the future of our work, we will consider noisy issues generated from the real environments by employing denoising technologies, like DCSR [60].…”