Abstract
Recently, many super-resolution algorithms have been proposed to recover high-resolution images to improve visualization and help better analyze images. Among them, total variation regularization (TV) methods have been proven to have a good effect in retaining image edge information. However, these TV methods do not consider the temporal correlation between images. Our algorithm designs a new TV regularization (TV2++) to take advantage of the time dimension information of the images, further improving the utilization of useful information in the images. In addition, the union of global low rank regularization and TV regularization further enhances the image super-resolution recovery. And we extend the exponential-type penalty (ETP) function on singular values of a matrix to enhance low-rank matrix recovery. A novel image super-resolution algorithm based on the ETP norm and TV2++ regularization is proposed. And the alternating direction method of multipliers (ADMM) is applied to solve the optimization problems effectively. Numerous experimental results prove that the proposed algorithm is superior to other algorithms.
Highlights
The super-resolution technology [1,2,3,4] is to restore high-frequency details lost during hardware acquisition of images, thereby improving image quality, making the image more rich in texture and providing better visualization
We propose to use alternating direction method of multipliers (ADMM) to optimize the calculation of the model, which greatly improves the running speed and efficiency
We introduce the exponential-type penalty (ETP) global low-rank regularization [48, 50] and design a total variation regularization (TV) regularization on the space dimension and the time dimension (TV2++) to construct the basic framework of the algorithm
Summary
The super-resolution technology [1,2,3,4] is to restore high-frequency details lost during hardware acquisition of images, thereby improving image quality, making the image more rich in texture and providing better visualization. Super-resolution technology is widely applied to face recognition [1], CT diagnosis [2], high-definition television [3], remote sensing image [4, 5], etc. These studies based on image processing are of great significance to the construction of the Internet of Things (IoT) [6] and smart cities. The system based on cloud environment has many shortcomings [8, 9] In this part, we first introduce a basic model in the TV methods, and we introduce the exponential-type penalty function and IRNN algorithm for solving the nonconvex low-rank minimization problem. N represents the noise which influencing our observation results
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