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Zero - DCE Deep Learning Framework for Low-Light Image Enhancement with Automated Visibility Classification and Performance Analysis
Authors
Sonali Sunil Gitte,Dr.Sushil V.Kulkarni
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Keywords
Low-light image enhancement, Zero-DCE, deep curve estimation, visibility classification, unsupervised learning, PSNR, SSIM, convolutional neural network, LOL dataset, image quality assessment, computer vision.
Abstract
Low-light image enhancement is a fundamental challenge in computer vision with critical applications spanning surveillance, autonomous navigation, medical imaging, and consumer photography. This paper presents Zero-DCE+, a comprehensive deep learning framework integrating Zero-Reference Deep Curve Estimation (Zero-DCE) for unsupervised low-light enhancement with an automated three-class visibility classification system. The proposed enhancement network comprises a lightweight convolutional architecture (~79,416 parameters) that estimates pixel-wise higher-order curve parameters across eight iterative refinement steps, guided by four complementary unsupervised loss functions (spatial consistency, exposure control, color constancy, and illumination smoothness). A separate visibility classifier employing CNN with batch normalization categorizes enhanced images as Low, Medium, or High visibility. Evaluated on the LOL benchmark dataset (485 training / 15 test pairs), the proposed framework achieves a competitive average PSNR of 14.5 dB and SSIM of 0.61, outperforming the original Zero-DCE baseline on SSIM while maintaining comparable PSNR. The visibility classifier attains 91.8% accuracy with ROC-AUC of 0.97. The complete pipeline operates at sub-200ms inference time on standard CPU hardware and is deployed as an interactive Streamlit web application. This work demonstrates that zero-reference learning can achieve competitive enhancement quality while remaining practical for resource-constrained deployment without GPU infrastructure.
IJCRT's Publication Details
Unique Identification Number - IJCRT2606563
Paper ID - 310720
Author type - Indian Author
Page Number(s) - f222-f233
Pubished in - Volume 14 | Issue 6 | June 2026
DOI (Digital Object Identifier) -
No Of Downloads - 74
Author Country - India, 431515, Parli Vaijnath, Parli Vaijnath, 431515, Science and Technology
Publisher Name - IJPUBLICATION | www.ijcrt.org | ISSN : 2320-2882
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Sonali Sunil Gitte,Dr.Sushil V.Kulkarni, "Zero - DCE Deep Learning Framework for Low-Light Image Enhancement with Automated Visibility Classification and Performance Analysis", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.14, Issue 6, pp.f222-f233, June 2026, Available at :http://www.ijcrt.org/papers/IJCRT2606563.pdf
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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