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Abstract
Owing to the limitations of imaging principles and system imaging characteristics, infrared images generally have some shortcomings, such as low resolution, insufficient details, and blurred edges. Therefore, it is of practical significance to improve the quality of infrared images. To make full use of the information on adjacent points, preserve the image structure, and avoid staircase artifacts, this paper proposes a super-resolution reconstruction method for infrared images based on quaternion total variation and high-order overlapping group sparse. The method uses a quaternion total variation method to utilize the correlation between adjacent points to improve image anti-noise ability and reconstruction effect. It uses the sparsity of a higher-order gradient to reconstruct a clear image structure and restore smooth changes. In addition, we performed regularization by using the denoising method, alternating direction method of multipliers, and fast Fourier transform theory to improve the efficiency and robustness of our method. Our experimental results show that this method has excellent performance in objective evaluation and subjective visual effects.
Highlights
Image super-resolution reconstruction (SRR) uses digital signal processing to generate high-resolution (HR) images from a single or multiple frames of low-resolution (LR) images, mainly through the super-resolution method
Using the Lp quasinorm instead of the L1 norm, we have proposed a method for infrared image deblurring with an overlapping group sparse total variation method, in which the Lp quasinorm introduces another degree of freedom, better describes image sparsity characteristics, and improves image restoration [41]
regularization by denoising (RED)-HOGS4 is compared with different noise levels and Gaussian blur conditions with several other methods, including the median filter transform (MFT) [47], RED-total variation (TV) [17], RED-total generalized variation (TGV) [20], and RED-overlapping group sparse total variation (OGSTV) [36]
Summary
Image super-resolution reconstruction (SRR) uses digital signal processing to generate high-resolution (HR) images from a single or multiple frames of low-resolution (LR) images, mainly through the super-resolution method. Image super-resolution reconstruction can efficiently utilize the potential value of existing image data and has applications such as military remote sensing reconnaissance [1], target tracking and monitoring [2,3,4], target location and recognition [5], astronomical observation [6], and medical imaging [7]. There are three types of super-resolution reconstruction methods: based on regular terms representation, learning-based methods, and partial differential equation-based methods. Learning-based image super-resolution reconstruction has been studied extensively in the recent years. Based on the convolutional neural network (CNN), Lim proposed an enhanced deep super-resolution network (EDSR) by removing unnecessary modules [8]. Dong redesigned the super-resolution CNN (SRCNN) structure by introducing a deconvolution layer at the end of the network, reformulating the mapping layer, adopting smaller filter sizes [9]. Xu proposed a Sensors 2019, 19, 5139; doi:10.3390/s19235139 www.mdpi.com/journal/sensors
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