Year: 2026
Type: article
Abstract: Images captured under low light situations suffer from low contrast and low visibility which can effect in bad manner on computer tasks to overcome this problem, enhancing low light image needed as pre-processing. This paper is presented a trainable model for low light image enhancing. It is based on multi scale Retinex by using deep learning and convolutional neural network (CNN) algorithm. Public (LOL) dataset has been used to train this model, consisted from 500 colored, low light images. Convolutional neural network bullied-up from eleven layers. SSIM and PSNR has been used to evaluate this model showing that average value of SSIM is (o.8) and average value of PSNR is and (21).
Authors: B. Satish Babu, D. Dharani, G. Sobha Serena, G. Geetha Sri, J. Siva Kumari
Language: English
Topic: Image Enhancement Techniques
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W7140024658
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