Algorithm for image processing using a frequency separation method

Radulescu, Virginia; Maican, Canelia Adela

doi:10.1109/iccc54292.2022.9805961

Cited by 1 publication

(1 citation statement)

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“…The light effect suppression network further suppresses the light effect while enhancing the illumination of dark regions; structural and high-frequency coherence losses were proposed in order to recover background details and reduce illusions and artifacts. Radulescu et al [14] decomposed an image into representations in the frequency domain in order to refine the image rendering. Various image decomposition methods provide new ideas for the light effect decomposition of the special power operation site in this paper.…”

Section: Related Workmentioning

confidence: 99%

Low-Light Image Enhancement Method for Electric Power Operation Sites Considering Strong Light Suppression

Xi,

Zhang,

Wang

2023

Applied Sciences

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Insufficient light, uneven light, backlighting, and other problems lead to poor visibility of the image of an electric power operation site. Most of the current methods directly enhance the low-light image while ignoring local strong light that may appear in the electric power operation site, resulting in overexposure and a poor enhancement effect. Aiming at the above problems, we propose a low-light image enhancement method for electric power operation sites by considering strong light suppression. Firstly, a sliding-window-based strong light judgment method was designed, which used a sliding window to segment the image, and a brightness judgment was performed based on the average value of the deviation and the average deviation of the subimages of the grayscale image from the strong light threshold. Then, a light effect decomposition method based on a layer decomposition network was used to decompose the light effect of RGB images with the presence of strong light and eliminate the light effect layer. Finally, a Zero-DCE (Zero-Reference Deep Curve Estimation) low-light enhancement network based on a kernel selection module was constructed to enhance the low-light images with reduced or no strong light interference. Comparison experiments using the electric power operation private dataset and the SICE (Single Image Contrast Enhancement) Part 2 public dataset showed that our proposed method outperformed the current state-of-the-art low-light enhancement methods in terms of both subjective visual effects and objective evaluation metrics, which effectively improves the image quality of electric power operation sites in low-light environments and provides excellent image bases for other computer vision tasks, such as the estimation of operators’ posture.

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Section: Related Workmentioning

confidence: 99%