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International Journal of Computing and Artificial Intelligence

Impact Factor (RJIF): 5.57, P-ISSN: 2707-6571, E-ISSN: 2707-658X
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2025, Vol. 6, Issue 1, Part A

Image enhancement in low light conditions utilising a basic network structure


Author(s): Potturi Reshma, Ronda Venkata Raja Mohan Reddy, Ponnada Spandana, Mohammad Lubna Gazal and Parise Likshmitha

Abstract: Unfactory lighting can influence taken photos in low light levels. LLIE, or low-light image enhancement, is a digital image processing technique used in digital photography to produce natural normal-light photographs from the low-light source. Reducing noise and artefacts, retaining edges and textures, and replicating natural brightness and colour constitute three key goals covered by LLIE. Many kinds of study have concentrated recently on deep-learning-based methods with outstanding performance. One main drawback of these approaches, though, is that intricate network architectures cause significant inference times. We present a basic network with effective modules to handle the trade-off between the performance and implementation time. We make advantage of a relative global histogram. In image processing, stretching is a method applied to improve image contrast. The technique is modifying an image's intensity values such that they cover a desired range. Faster inference time and better performance than traditional LLIE techniques define the suggested method.

DOI: 10.33545/27076571.2025.v6.i1a.134

Pages: 56-60 | Views: 531 | Downloads: 110

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International Journal of Computing and Artificial Intelligence
How to cite this article:
Potturi Reshma, Ronda Venkata Raja Mohan Reddy, Ponnada Spandana, Mohammad Lubna Gazal, Parise Likshmitha. Image enhancement in low light conditions utilising a basic network structure. Int J Comput Artif Intell 2025;6(1):56-60. DOI: 10.33545/27076571.2025.v6.i1a.134
International Journal of Computing and Artificial Intelligence

International Journal of Computing and Artificial Intelligence

International Journal of Computing and Artificial Intelligence
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