Infrared Image Edge Extraction of Cable Terminal Based on Improved Eight Direction Sobel Operator

Niu, Aiqun; Guo, Bin; Xu, Chao; Song, Dalei; E, Xu

doi:10.1109/powercon.2018.8602297

Cited by 5 publications

(2 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Sobel operator 22 adds weights on the basis of the Prewitt operator and considers that the closer adjacent pixels have a greater impact on the current pixel. Niu et al 13 . proposed the eight-direction Sobel operator, extending the Sobel operator to eight directions, i.e., the x-axis direction, 45 deg, y-axis direction, 135 deg, 180 deg, 225 deg, 270 deg, and 315 deg.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…Large scale is beneficial to handle effects of anisotropic blur and mis-registration while small scale can better measure the boundary of focused and non-focused regions. A Sobel gradient operator 13 is often used to detect image edges, and 14 also used it to obtain the gradient value of a source image. Gradient-based fusion methods usually belong to spatial domain methods, and some deep-learning-based methods 14 use gradients as part of the loss function.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Multi-focus image fusion based on gradient tensor HOSVD

Wang

Cao

et al. 2023

J. Electron. Imag.

View full text Add to dashboard Cite

.Multi-focus image fusion is the process of fusing images of the same scene with different focus ranges. However, pixel misclassification and artifacts caused by noise have been unavoidable problems in fusion methods. To overcome this problem, we propose gradient tensor high-order singular value decomposition (HOSVD) to fuse multi-focus images. The eight-direction Sobel gradient operator is used to obtain second-order gradients in different directions. Tensor-based information processing can effectively represent high-dimensional information and extract structural information from multi-focus images. Therefore, second-order gradient matrices of different images are formed into tensors. The gradient tensor of the images is transformed by HOSVD to obtain the core tensor and factor matrixes. The images are better represented by multiplying the core tensor with the factor matrix corresponding to the direction of the image. In the core tensor, the larger singular values represent basic information and the small singular values high-frequency information. The noise generally belongs to high-frequency information, so we select the larger singular values in the core tensor as activity level variables of the image to reduce noise. Experimental results demonstrate that the proposed fusion method can effectively reduce noise effects and achieve state-of-the-art fusion performance in both objective metrics and visual quality.

show abstract