Image Enhancement for Surveillance Video of Coal Mining Face Based on Single-Scale Retinex Algorithm Combined with Bilateral Filtering

A Comprehensive Survey on Visual Perception Methods for Intelligent Inspection of High Dam Hubs

There are many high dam hubs in the world, and the regular inspection of high dams is a critical task for ensuring their safe operation. Traditional manual inspection methods pose challenges related to the complexity of the on-site environment, the heavy inspection workload, and the difficulty in manually observing inspection points, which often result in low efficiency and errors related to the influence of subjective factors. Therefore, the introduction of intelligent inspection technology in this context is urgently necessary. With the development of UAVs, computer vision, artificial intelligence, and other technologies, the intelligent inspection of high dams based on visual perception has become possible, and related research has received extensive attention. This article summarizes the contents of high dam safety inspections and reviews recent studies on visual perception techniques in the context of intelligent inspections. First, this article categorizes image enhancement methods into those based on histogram equalization, Retinex, and deep learning. Representative methods and their characteristics are elaborated for each category, and the associated development trends are analyzed. Second, this article systematically enumerates the principal achievements of defect and obstacle perception methods, focusing on those based on traditional image processing and machine learning approaches, and outlines the main techniques and characteristics. Additionally, this article analyzes the principal methods for damage quantification based on visual perception. Finally, the major issues related to applying visual perception techniques for the intelligent safety inspection of high dams are summarized and future research directions are proposed.

M. DR. Low-Light Image Enhancement Based on Generative Adversarial Network

Image enhancement is considered to be one of the complex tasks in image processing. When the images are captured under dim light, the quality of the images degrades due to low visibility degenerating the vision-based algorithms' performance that is built for very good quality images with better visibility. After the emergence of a deep neural network number of methods has been put forward to improve images captured under low light. But, the results shown by existing low-light enhancement methods are not satisfactory because of the lack of effective network structures. A low-light image enhancement technique (LIMET) with a fine-tuned conditional generative adversarial network is presented in this paper. The proposed approach employs two discriminators to acquire a semantic meaning that imposes the obtained results to be realistic and natural. Finally, the proposed approach is evaluated with benchmark datasets. The experimental results highlight that the presented approach attains state-of-the-performance when compared to existing methods. The models' performance is assessed using Visual Information Fidelitysse, which assesses the generated image's quality over the degraded input. VIF obtained for different datasets using the proposed approach are 0.709123 for LIME dataset, 0.849982 for DICM dataset, 0.619342 for MEF dataset.

Joint Dedusting and Enhancement of Top-Coal Caving Face via Single-Channel Retinex-Based Method with Frequency Domain Prior Information

Affected by the uneven concentration of coal dust and low illumination, most of the images captured in the top-coal caving face have low definition, high haze and serious noise. In order to improve the visual effect of underground images captured in the top-coal caving face, a novel single-channel Retinex dedusting algorithm with frequency domain prior information is proposed to solve the problem that Retinex defogging algorithm cannot effectively defog and denoise, simultaneously, while preserving image details. Our work is inspired by the simple and intuitive observation that the low frequency component of dust-free image will be amplified in the symmetrical spectrum after adding dusts. A single-channel multiscale Retinex algorithm with color restoration (MSRCR) in YIQ space is proposed to restore the foggy approximate component in wavelet domain. After that the multiscale convolution enhancement and fast non-local means (FNLM) filter are used to minimize noise of detail components while retaining sufficient details. Finally, a dust-free image is reconstructed to the spatial domain and the color is restored by white balance. By comparing with the state-of-the-art image dedusting and defogging algorithms, the experimental results have shown that the proposed algorithm has higher contrast and visibility in both subjective and objective analysis while retaining sufficient details.

Unsteady State Lightweight Iris Certification Based on Multi-Algorithm Parallel Integration

Aimed at the one-to-one certification problem of unsteady state iris at different shooting times, a multi-algorithm parallel integration general model structure is proposed in this paper. The iris in the lightweight constrained state affected by defocusing, deflection, and illumination is taken as the research object, the existing algorithms are combined into the model structure effectively, and a one-to-one certification algorithm for lightweight constrained state unsteady iris was designed based on multi-algorithm integration and maximum trusted decision. In this algorithm, a sufficient number of iris internal feature points from the unstable state texture were extracted as effective iris information through the image processing layer composed of various filtering processing algorithms, thereby eliminating defocused interference. In the feature recognition layer, iris deflection interference was excluded by the improved methods of Gabor and Hamming and Haar and BP for the stable features extracted by the image processing layer, and two certification results were obtained by means of parallel recognition. The correct number of certifications for an algorithm under a certain lighting condition were counted. The method with the most correct number was set as the maximum trusted method under this lighting condition, and the results of the maximum trusted method were taken as the final decision, thereby eliminating the effect of illumination. Experiments using the JLU and CASIA iris libraries under the prerequisites in this paper show that the correct recognition rate of the algorithm can reach a high level of 98% or more, indicating that the algorithm can effectively improve the accuracy of the one-to-one certification of lightweight constrained state unsteady iris. Compared with the latest architecture algorithms, such as CNN and deep learning, the proposed algorithm is more suitable for the prerequisites presented in this paper, which has good environmental inclusiveness and can better improve existing traditional algorithms’ effectiveness through the design of a parallel integration model structure.

Blind Deblurring of Saturated Images Based on Optimization and Deep Learning for Dynamic Visual Inspection on the Assembly Line

Image deblurring can improve visual quality and mitigates motion blur for dynamic visual inspection. We propose a method to deblur saturated images for dynamic visual inspection by applying blur kernel estimation and deconvolution modeling. The blur kernel is estimated in a transform domain, whereas the deconvolution model is decoupled into deblurring and denoising stages via variable splitting. Deblurring predicts the mask specifying saturated pixels, which are then discarded, and denoising is learned via the fast and flexible denoising network (FFDNet) convolutional neural network (CNN) at a wide range of noise levels. Hence, the proposed deconvolution model provides the benefits of both model optimization and deep learning. Experiments demonstrate that the proposed method suitably restores visual quality and outperforms existing approaches with good score improvements.

Image Enhancement for Inspection of Cable Images Based on Retinex Theory and Fuzzy Enhancement Method in Wavelet Domain

Inspection images of power transmission line provide vision interaction for the operator and the environmental perception for the cable inspection robot (CIR). However, inspection images are always contaminated by severe outdoor working conditions such as uneven illumination, low contrast, and speckle noise. Therefore, this paper proposes a novel method based on Retinex and fuzzy enhancement to improve the image quality of the inspection images. A modified multi-scale Retinex (MSR) is proposed to compensate the uneven illumination by processing the low frequency components after wavelet decomposition. Besides, a fuzzy enhancement method is proposed to perfect the edge information and improve contrast by processing the high frequency components. A noise reduction procedure based on soft threshold is used to avoid the noise amplification. Experiments on the self-built standard test dataset show that the algorithm can improve the image quality by 3–4 times. Compared with several other methods, the experimental results demonstrate that the proposed method can obtain better enhancement performance with more homogeneous illumination and higher contrast. Further research will focus on improving the real-time performance and parameter adaptation of the algorithm.

Enhancement of Low Contrast Images Based on Effective Space Combined with Pixel Learning

Images captured in bad conditions often suffer from low contrast. In this paper, we proposed a simple, but efficient linear restoration model to enhance the low contrast images. The model’s design is based on the effective space of the 3D surface graph of the image. Effective space is defined as the minimum space containing the 3D surface graph of the image, and the proportion of the pixel value in the effective space is considered to reflect the details of images. The bright channel prior and the dark channel prior are used to estimate the effective space, however, they may cause block artifacts. We designed the pixel learning to solve this problem. Pixel learning takes the input image as the training example and the low frequency component of input as the label to learn (pixel by pixel) based on the look-up table model. The proposed method is very fast and can restore a high-quality image with fine details. The experimental results on a variety of images captured in bad conditions, such as nonuniform light, night, hazy and underwater, demonstrate the effectiveness and efficiency of the proposed method.