A Methodological Approach for Inferring Urban Indicators Through Computer Vision

Paiva, Sara; Santos, Diogo; Rossetti, Rosaldo J. F.

doi:10.1109/isc2.2018.8656934

Cited by 1 publication

(2 citation statements)

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“…Image processing has received a lot of attention from researchers as a result of the advancement of computer vision, and it has been used in a variety of industries, including artificial intelligence [1], smart cities [2], the power industry [3], and the military [4]. Image acquisition, on the other hand, is easily influenced by the environment, particularly in low-light situations such as darkness, overcast days, and obstructed light, when the overall visual impact is gloomy due to the lack of reflected light, and noise and color imbalance can develop [5].…”

Section: Introductionmentioning

confidence: 99%

“…To begin, the curve transformation of the BHS function is increased using the error function erf, which effectively improves the image lightness. Then, instead of 2/π, a hyperparameter µ is used to adjust the degree of lightness amplification, the greater the µ is, the brighter the output image is, and the range of µ is experimentally known as [2,7]. Furthermore, employing 0.5 × I 3 allows the image tones to be more similar to the tones of the scene as observed by the human eye.…”

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Study on a Low-Illumination Enhancement Method for Online Monitoring Images Considering Multiple-Exposure Image Sequence Fusion

Zhao

Jiang

et al. 2023

Electronics

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In order to improve the problem of low image quality caused by insufficient illumination, a low-light image enhancement method with robustness is proposed, which can effectively handle extremely dark images while achieving good results for scenes with insufficient local illumination. First, we expose the images to different degrees to form a multi-exposure image sequence; then, we introduce global-based luminance weights and contrast-based gradient weights to fuse the multi-exposure image sequence; finally, we use a bootstrap filter to suppress the noise that may occur during the image processing. We employ pertinent assessment criteria, such as the Peak Signal to Noise Ratio (PSNR), Structural Similarity (SSIM), the Average Gradient (AG), and the Figure Definition (FD), to assess how well the method enhances. Experimental results show that PSNR (31.32) and SSIM (0.74) are the highest in pretty dark scenes compared to most conventional algorithms such as MF, BIMEF, LECARM, etc. Similarly, in processing uneven illumination such as “moonlit night” images, the AG (10.21) and the FD (14.54) are at maximum. In addition, other evaluation metrics such as Shannon (SH) are optimal in the above scenarios. In addition, we apply the algorithm in this paper to the online monitoring images of electric power equipment, which can improve the image lightness while recovering the detail information. The algorithm has strong robustness in extremely dark images and natural low-light images, and the enhanced images have minimal distortion and best appearance in different low-light scenes.

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