International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Call for Papers | Fully Refereed | Open Access | Double Blind Peer Reviewed

ISSN: 2319-7064


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Informative Article | Science and Technology | India | Volume 10 Issue 3, March 2021


Advances in Deep Learning for Image Processing: Techniques, Challenges, and Applications

Raghavendra Rao Sangarsu [3]


Abstract: In the domain of image processing, deep learning has assumed the position of the spearhead of innovation, creating revolutionary changes in numerous fields, such as image categorization, object recognition, and image generation. This white paper aims at developing a more detailed overview of the modern deep learning methods which are primarily designed for image processing. Analyzing the essential aspects of CNNs, GANs, and RNNs we wish to reveal innovative achievements in this area. In this white paper, it highlights a broad variety of domains, where these deep learning techniques have demonstrated remarkable progress. In the field of medical imaging, where CNNs help in the diagnosis and prognosis of the disease, to satellite imagery analysis, where GANs improve the generation of synthetic realistic data, and computer vision, where RNNs allow for better video analysis, deep learning is redefining the limits of image processing. Development should also be highlighted as an issue that the integration of deep learning into the image processing pipeline may bring along with it, including data accessibility, model interpretability, and computing resources. Accordingly, acknowledging such issues and staying updated with new trends, researchers and practitioners will be able to fully utilize deep learning and use it to solve complex image processing problems and carry out innovation in many different spheres of life. Deep learning is here to stay at the cutting edge of image processing, transforming our abilities and perspectives in this ever - changing discipline.


Keywords: deep learning, image processing, CNNs Convolutional Neural Networks, GANs Generative Adversarial Networks, innovation


Edition: Volume 10 Issue 3, March 2021,


Pages: 1960 - 1963


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