People are active during the nighttime and capture many photos. Due to the low-light nature of the environment, captured images tend to appear dimmed with imbalanced illumination, low contrast, noise, and a lack of vibrant colors. For this purpose, this paper presents a direct and practical approach to improving the lighting of night images based on the single-scale retinex model and using image processing methods and other statistical approaches. The proposed algorithm begins by converting the image from the RGB to the HSI model. Then, it enhances only the I channel while preserving the H and S channels. Thus, processing on the I channel begins with estimating the illumination version of the image and calculating the logarithms of both the illuminated and original image, like the SSR model. Then, it subtracts these two images using a subtraction of logarithmic image processing. Subsequently, the cumulative distribution function of the Gumble probability distribution is applied, and the resulting output is further treated using a logarithmic transformation method. That produces the processed I channel, which combines with the conserved H and S channels to give the HSI image. Ultimately, it converts the image to RGB format. The proposed algorithm is applied to two datasets of nighttime images, compares their performance to seven different contemporary algorithms, and evaluates the results of the compares using three specific metrics of quality evaluation based on the results generated, deducing that the suggested algorithm enhanced the brightness of night images and surpassed other algorithms in terms of execution time, and image assessment methods. Additionally, it exceeded them in all metrics used.
Key words: Nighttime, Image Enhancement, Single Scale Retinex, Statistical Methods.
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