Infrared Image Denoising via L1/2 Sparse Representation over Learned Dictionary

Luo, Yihang; Wang, Shengqian; Deng, Chengzhi

doi:10.1109/iscid.2014.39

Cited by 1 publication

(1 citation statement)

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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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