Enhancing Image Quality With Deep Learning: Techniques And Applications
DOI:
https://doi.org/10.31961/eltikom.v8i2.1242Keywords:
convolutional neural networks, deep learning, denoising, image enhancementAbstract
The emergence of deep learning has transformed numerous fields, particularly image processing, where it has substantially enhanced image quality. This paper provides a structured overview of the objectives, methods, results, and conclusions of deep learning techniques for image enhancement. It examines deep learning methodologies and their applications in improving image quality across diverse domains. The discussion includes state-of-the-art algorithms such as Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), and Autoencoders, highlighting their applications in medical imaging, photography, and remote sensing. These methods have demonstrated notable impacts, including noise reduction, resolution enhancement, and contrast improvement. Despite its significant promise, deep learning faces challenges such as computational complexity and the need for large annotated datasets. outlines future research directions to overcome these limitations and further advance deep learning's potential in image enhancement.
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Copyright (c) 2024 Hewa Majeed Zangana, Firas Mahmood Mustafa, Ayaz Khalid Mohammed, Naaman Omar
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