Blur Kernel Estimation and Non-Blind Super-Resolution for Power Equipment Infrared Images by Compressed Sensing and Adaptive Regularization

Infrared sensing technology is more and more widely used in the construction of power Internet of Things. However, due to cost constraints, it is difficult to achieve the large-scale installation of high-precision infrared sensors. Therefore, we propose a blind super-resolution method for infrared images of power equipment to improve the imaging quality of low-cost infrared sensors. If the blur kernel estimation and non-blind super-resolution are performed at the same time, it is easy to produce sub-optimal results, so we chose to divide the blind super-resolution into two parts. First, we propose a blur kernel estimation method based on compressed sensing theory, which accurately estimates the blur kernel through low-resolution images. After estimating the blur kernel, we propose an adaptive regularization non-blind super-resolution method to achieve the high-quality reconstruction of high-resolution infrared images. According to the final experimental demonstration, the blind super-resolution method we proposed can effectively reconstruct low-resolution infrared images of power equipment. The reconstructed image has richer details and better visual effects, which can provide better conditions for the infrared diagnosis of the power system.

Research on Blind Super-Resolution Technology for Infrared Images of Power Equipment Based on Compressed Sensing Theory

Infrared images of power equipment play an important role in power equipment status monitoring and fault identification. Aiming to resolve the problems of low resolution and insufficient clarity in the application of infrared images, we propose a blind super-resolution algorithm based on the theory of compressed sensing. It includes an improved blur kernel estimation method combined with compressed sensing theory and an improved infrared image super-resolution reconstruction algorithm based on block compressed sensing theory. In the blur kernel estimation method, we propose a blur kernel estimation algorithm under the compressed sensing framework to realize the estimation of the blur kernel from low-resolution images. In the estimation process, we define a new norm to constrain the gradient image in the iterative process by analyzing the significant edge intensity changes before and after the image is blurred. With the norm, the salient edges can be selected and enhanced, the intermediate latent image generated by the iteration can move closer to the clear image, and the accuracy of the blur kernel estimation can be improved. For the super-resolution reconstruction algorithm, we introduce a blur matrix and a regular total variation term into the traditional compressed sensing model and design a two-step total variation sparse iteration (TwTVSI) algorithm. Therefore, while ensuring the computational efficiency, the boundary effect caused by the block processing inside the image is removed. In addition, the design of the TwTVSI algorithm can effectively process the super-resolution model of compressed sensing with a sparse dictionary, thereby breaking through the reconstruction performance limitation of the traditional regularized super-resolution method of compressed sensing due to the lack of sparseness in the signal transform domain. The final experimental results also verify the effectiveness of our blind super-resolution algorithm.

Single Image Super Resolution of Blurred Natural Images Using Blur Kernel Estimation Combined with Super Resolution Convolution Neural Network

Image De-blurring and super-resolution (SR) are computer vision tasks aiming to restore image detail and spatial scale, respectively. Despite the significant improvement in image quality resulting from improvement in optical sensors and general electronics, camera shake blur significantly undermines the quality of hand-held photographs. We evaluated the state-of-the-art super-resolution convolution neural network(SR-CNN) architecture and proposed a new architecture for SR application inspired by SR-CNN combined with De-blurring. This paper focus super resolution of a de-focussed and motion blurred natural images. Unlike most de-blurring methods that attempt to solve an inverse problem through a variational formulation, deblurring method applied in this work directly estimates the blur kernel by modeling statistical irregularities in the power spectrum of blurred natural images. Extensive experiments indicate that the proposed method not only generates remarkably clear HR images, but also achieves compelling results in PSNR, MSE and SSIM quantitatively.

Super Resolution with Kernel Estimation and Dual Attention Mechanism

Convolutional Neural Networks (CNN) have led to promising performance in super-resolution (SR). Most SR methods are trained and evaluated on predefined blur kernel datasets (e.g., bicubic). However, the blur kernel of real-world LR image is much more complex. Therefore, the SR model trained on simulated data becomes less effective when applied to real scenarios. In this paper, we propose a novel super resolution framework based on blur kernel estimation and dual attention mechanism. Our network learns the internal relations from the input image itself, thus the network can quickly adapt to any input image. We add the blur kernel estimation structure into the network, correcting the inaccurate blur kernel to generate high quality images. Meanwhile, we propose a dual attention mechanism to restore the texture details of the image, adaptively adjusting the features of the image by considering the interdependencies both in channel and spatial. The combination of blur kernel estimation and attention mechanism makes our network perform well for complex blur images in practice. Extensive experiments show that our method (KASR) achieves promising accuracy and visual improvements against most existing methods.