Insulator Infrared Image Denoising Method Based on Wavelet Generic Gaussian Distribution and MAP Estimation

He, Han; Lee, Wei-Jen; Luo, Diansheng; Cao, Yijia

doi:10.1109/tia.2017.2691309

Cited by 31 publications

(6 citation statements)

References 22 publications

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“…α is the estimator of α. Sparse means that the sparse representation coefficient α contains more zeros to ensure the sparse representation coefficient α is sparse enough; sparse representation means using fewer column vectors of learning dictionary D when representing the reconstructed image by the learning dictionary D and sparse representation coefficients α; T is the iterative threshold [27]- [28], which is determined by ε and σ and can solve T by the maximum a posteriori (MAP) algorithm [29].…”

Section: A Sparse Representationmentioning

confidence: 99%

Double-Norm Constrained Image Denoising Algorithm Based on Dictionary Learning Sparsity and FCM Structure Clustering

Dai

2022

IEEE Access

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To solve the problem of image smoothness and fuzzy edge texture information after image denoising, proposed a new image denoising method based on dictionary learning. Firstly, the external cycling principal component analysis reduces the dimensions of image data while retaining the main data and constructing the learning dictionary. Secondly, used the fuzzy c-means structure clustering method internally to implement structural constraints on learning dictionary training, which considered the internal structure of image pixels. Then, the learning dictionary under the double constraints of sparse and structural clustering is obtained by internal and external iteration. Finally, the sparse representation coefficient and redundancy dictionary are obtained by the orthogonal matching pursuit method and alternate direction multiplier method, and the denoised images are estimated and updated according to sparse coding theory. Using the grayscale image from Set12 data set, color image from the CBSD68 data set, real noise from RENOIR data set, and texture image from USC-SIPI data set. The experimental results show that compared with the model-based algorithms (KSVD, ISKR, EPLL, NCSR, and LR-GSC) and the learningbased algorithms(DnCNN, IRCNN, and FFDNet), the proposed algorithm preserves the edge and texture information of the image better, and achieves better subjective visual effects and objective numerical results, especially for the image with complex structure and content, and the running time is much less than learningbased algorithms.

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Section: A Sparse Representationmentioning

confidence: 99%

Double-Norm Constrained Image Denoising Algorithm Based on Dictionary Learning Sparsity and FCM Structure Clustering

Dai

2022

IEEE Access

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“…With regard to noise reduction, mean filtering [30], median filtering [31] and adaptive filtering [32, 33] are commonly used, and a range of advanced algorithms upon them came out with different superiorities [34–36]. Besides, wavelet transform [37], over‐complete sparse representation [38, 39], convolutional neural network [40] have also been introduced and greatly improved the accuracy and speed of image de‐noising.…”

Section: Machine‐assisted Fault Diagnosismentioning

confidence: 99%

Infrared thermography‐based diagnostics on power equipment: State‐of‐the‐art

et al. 2020

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As a non‐contact temperature distribution measurement method, infrared thermography (IRT) has emerged as an indispensable tool in condition monitoring and fault diagnosis of electrical equipment based on the absolute and relative temperature values. Manual fault inspection, as an expert‐experiences based evaluation method, has formed a mature technical scheme with a large number of application cases. However, the efficiency and accuracy of manual fault inspection are being challenged by the rapid growth in the number of equipment in power grid. The situation is improving with the advanced of image processing technique. Machine‐assisted fault diagnosis provides a novel method to assist human beings to complete fault diagnosis under the intervention of human prior knowledge. However, the limitations of infrared images bring challenges to image analysis processing especially target detection. In pursuit of automatic fault diagnosis, deep learning algorithms are introduced to achieve target detection in the complex environment. This study reviews the development of IRT‐based diagnostics beginning with the general procedures, objects, and limitations of IRT‐based fault inspection, and then gives an insight into the popular machine‐assisted fault diagnosis as well as image‐based intelligent fault identification. In addition, the future recommendations of IRT are also provided from construction of intelligent infrared detection system, establishment of an open and shared infrared image database and comprehensive utilization of joint visualization diagnosis technology.

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“…1450 images are used to build the criterion image library (covers all images with zero-value insulator at different positions on the string) and the rest 350 images are used as testing samples. For infrared images of insulators that have high noise and low contrast, a wavelet denoising method based on Wavelet and MAP estimation [19] is used to depress the noise and improved the quality of the image. Then, the improved SIFT method presented is used to extract the insulator string features and pre-match the string with the ones in the zero-resistance insulator image library.…”

Section: Test Setup and Image Acquisitionmentioning

confidence: 99%

A Contactless Zero-Value Insulators Detection Method Based on Infrared Images Matching

Wang

et al. 2020

IEEE Access

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A contactless method based on infrared image matching is presented for zero-value insulator detection on outdoor porcelain insulator string. An improved SIFT (scale-invariant feature transformation) method is developed to extract features and accomplish the pre-matching between the insulator string to be detected and the standard string in the image library. An improved RANSAC (random sampling consistency) algorithm is developed to remove mismatching points and achieve more accurate and faster detection. Firstly, an adaptive circle window is designed to improve the sensitivity of the SIFT operator on arc features extraction from insulators. Then, the dimension of the feature is decreased from128 to 32 by dividing the circle into 4 fan-shaped regions and descript the feature on 8 directions to achieve the lower computation burden and high accuracy. For the SIFT method and RANSAC method uses Euclidean distance to measure the similarity between features extracted from the string to be detected and the string in the standard library, mismatching may be caused. Spatial geometric features of the neighborhood around the feature point are used to construct the error function to improve the RANSAC method and remove the mismatching points. Faster matching is obtained by a threshold control for decreasing the number of data checking for the consensus set data models. Testing results show that the presented method is effective in the detection of zero-value resistances for outdoor insulator string. Compare with the SIFT method and SIFT+RANSAC method, the presented method has higher accuracy and faster detection speed.INDEX TERMS Zero-value insulator detection, infrared imaging, image matching, adaptive circle template, spatial geometric features, prosumer infrastructure

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Insulator Infrared Image Denoising Method Based on Wavelet Generic Gaussian Distribution and MAP Estimation

Cited by 31 publications

References 22 publications

Double-Norm Constrained Image Denoising Algorithm Based on Dictionary Learning Sparsity and FCM Structure Clustering

Double-Norm Constrained Image Denoising Algorithm Based on Dictionary Learning Sparsity and FCM Structure Clustering

Infrared thermography‐based diagnostics on power equipment: State‐of‐the‐art

A Contactless Zero-Value Insulators Detection Method Based on Infrared Images Matching

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