AI-BASED NOISE REDUCTION IN ARTISTIC PHOTOGRAPHY
DOI:
https://doi.org/10.29121/shodhkosh.v6.i4s.2025.6841Keywords:
AI Denoising, Artistic Photography, U-Net, Gan Restoration, Transformer Models, Perceptual Quality MetricsAbstract [English]
The AI-driven noise-reduction has become a game-changer in artistic photography, which allows restoring, improving, and preserving the style of a wide variety of visual fields. Conventional methods of denoising like bilateral filtering, wavelet shrinkage, and non-local means tend to be at a loss on how to trade off noise with texture especially in artistic photography where grain, contrast changes, and tonal fading hold aesthetic value. Recent developments in deep learning and, in particular, Denoising Autoencoders (DAEs), U-Net models, GAN-based image restorers, and Transformer-hybrid models, have better capabilities and learn noise patterns and aesthetic features, jointly. These models make use of massive photography data, manufactured noise (Gaussian, Poisson, noise which depends on ISO), and actual imperfection of camera sensors to create strong noise-sensitive representations. This combination of the loss of perceptual information, attention, and multi-scale features aggregation is useful to preserve trivial elements of artistic effects like brush-like textures, film-grain like textures, skin color, and fine edges. Moreover, the noise reduction of AI makes the vintage and digital artworks sound or look better by restoring damaged film photographs, enhancing the portraits in low-light conditions, and AI-assistive creative processes in digital painting, conceptual art, and stylized image generation. Quantitative measures, such as PSNR, SSIM, LPIPS, and Delta-E, show that the methods are improving steadily over the classical methods, whereas qualitative analysis reveals that the methods preserve tone and form expressive details better.
References
Agrawal, S., Panda, R., Mishro, P. K., and Abraham, A. (2022). A Novel Joint Histogram Equalization Based Image Contrast Enhancement. Journal of King Saud University – Computer and Information Sciences, 34, 1172–1182. https://doi.org/10.1016/j.jksuci.2019.05.010
Archana, R., and Jeevaraj, P. E. (2024). Deep Learning Models for Digital Image Processing: A Review. Artificial Intelligence Review, 57, Article 11. https://doi.org/10.1007/s10462-023-10631-z
Badjie, B., Cecílio, J., and Casimiro, A. (2024). Adversarial Attacks and Countermeasures on Image Classification-Based Deep Learning Models in Autonomous Driving Systems: A Systematic Review. ACM Computing Surveys, 57(1), 1–52. https://doi.org/10.1145/3691625
Cai, Y., Zhang, W., Chen, H., and Cheng, K. T. (2025). Medianomaly: A Comparative Study of Anomaly Detection in Medical Images. Medical Image Analysis, 102, Article 103500. https://doi.org/10.1016/j.media.2025.103500
Chen, W., Feng, S., Yin, W., Li, Y., Qian, J., Chen, Q., and Zuo, C. (2024). Deep-Learning-Enabled Temporally Super-Resolved Multiplexed Fringe Projection Profilometry: High-Speed kHz 3D Imaging with Low-Speed Camera. PhotoniX, 5, Article 25. https://doi.org/10.1186/s43074-024-00139-2
Emek Soylu, B., Guzel, M. S., Bostanci, G. E., Ekinci, F., Asuroglu, T., and Acici, K. (2023). Deep-Learning-Based Approaches for Semantic Segmentation of Natural Scene Images: A Review. Electronics, 12(12), Article 2730. https://doi.org/10.3390/electronics12122730
Gano, B., Bhadra, S., Vilbig, J. M., Ahmed, N., Sagan, V., and Shakoor, N. (2024). Drone-Based Imaging Sensors, Techniques, and Applications in Plant Phenotyping for Crop Breeding: A Comprehensive Review. Plant Phenome Journal, 7, Article e20100. https://doi.org/10.1002/ppj2.20100
Gui, S., Song, S., Qin, R., and Tang, Y. (2024). Remote Sensing Object Detection in the Deep Learning Era: A Review. Remote Sensing, 16(2), Article 327. https://doi.org/10.3390/rs16020327
Lepcha, D. C., Goyal, B., Dogra, A., Sharma, K. P., and Gupta, D. N. (2023). A Deep Journey into Image Enhancement: A Survey of Current and Emerging Trends. Information Fusion, 93, 36–76. https://doi.org/10.1016/j.inffus.2022.12.012
Liu, Y., Bai, X., Wang, J., Li, G., Li, J., and Lv, Z. (2024). Image Semantic Segmentation Approach Based on DeepLabV3+ Network with an Attention Mechanism. Engineering Applications of Artificial Intelligence, 127, Article 107260. https://doi.org/10.1016/j.engappai.2023.107260
Ma, J., He, Y., Li, F., Han, L., You, C., and Wang, B. (2024). Segment Anything in Medical Images. Nature Communications, 15, Article 654. https://doi.org/10.1038/s41467-024-44824-z
Rao, B. S. (2020). Dynamic Histogram Equalization for Contrast Enhancement for Digital Images. Applied Soft Computing, 89, Article 106114. https://doi.org/10.1016/j.asoc.2020.106114
Vijayakumar, A., and Vairavasundaram, S. (2024). YOLO-Based Object Detection Models: A Review and its Applications. Multimedia Tools and Applications, 83, 83535–83574. https://doi.org/10.1007/s11042-024-18872-y
Wang, A., Chen, H., Liu, L., Chen, K., Lin, Z., and Han, J. (2025). YOLOv10: Real-Time End-to-End Object Detection. Advances in Neural Information Processing Systems, 37, 107984–108011. https://doi.org/10.52202/079017-3429
Zhang, P., Zhou, F., Wang, X., Wang, S., and Song, Z. (2024). Omnidirectional Imaging Sensor Based on Conical Mirror for Pipelines. Optics and Lasers in Engineering, 175, Article 108003. https://doi.org/10.1016/j.optlaseng.2023.108003
Zhao, T., Guo, P., and Wei, Y. (2024). Road Friction Estimation Based on Vision for Safe Autonomous Driving. Mechanical Systems and Signal Processing, 208, Article 111019. https://doi.org/10.1016/j.ymssp.2023.111019
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Copyright (c) 2025 Damanjeet Aulakh, Mr. Divya Piakaray, Priyadarshani Singh, Saudagar Subhash Barde, Deenadayalan T, Ms. Ipsita Dash
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