Shivani Gaikwad

Work place: Department of Computer Science and Engineering, K. E. Society‘s, Rajarambapu Institute of Technology, Rajaramnagar, Affiliated to Shivaji University Kolhapur, MH, India

E-mail: gaikwadshivani96@gmail.com

Website: https://orcid.org/0000-0002-9127-9152

Research Interests:

Biography

Shivani Gaikwad was born in Sangli, Maharashtra, India in 1996. She received the B.E. degree in computer Science and engineering and MTech. in computer Science and engineering from Shivaji University, Kolhapur in 2018 and 2022 respectively. Since 2022, she is working in as Platform Developer in Capgemini Technology Services India Limited, Pune. Her role involves designing, configuring, developing, troubleshooting, and implementing baseline and custom applications to enhance the platform. She has recognized as a Star of ServiceNow for her Exemplary Performance in Oct 2023 and Nov 2023. Her research interest includes Image processing and Machine Learning. She has published her research papers in renowned international conferences and journals.

Author Articles
Underwater Image Refinement: Color Distance and Image Formation Model (DIMFM)

By Shivani Gaikwad Sachin Patil

DOI: https://doi.org/10.5815/ijigsp.2024.06.01, Pub. Date: 8 Dec. 2024

Underwater photography is frequently used for research purpose in various domains. Domains caters to archaeology, surveillance of aquatic life movements, oceanic changes leading to alterations in weather and many more. Scientists are eager to investigate the mysterious undersea environment. For underwater surveys, archaeology departments and weather forecasting scientists obtain undersea photos. The underwater imagery however has low vision and contrast due to haze. The elimination of haze could be difficult because it depends on depth information that is unclear. Moreover, it’s challenging and complicated to clear the haze so as to enhance the underwater image. According to the investigation, fog removal algorithms currently in use do not take noise reduction approaches into account. Dehazing techniques have a hard time dealing with areas that are unevenly and excessively light. Therefore, it is vital to alter current techniques in order to make them more efficient. This work presents an innovative integrated underwater picture restoration technique. The proposed technique is in line to a pre-determined technique namely Underwater Image Formation Model. The new approach combines Bilateral Filtering, Contrast Limited Adaptive Histogram Equalization and Dark Channel Prior for better results. First, the underwater image undergoes bilateral filtering to eliminate color discrepancies. The improved image is output of the differentiation between forward and background channel. Further, the Contrast Limited Adaptive Histogram Equalizations methodology is used to produce contrast-enhanced images. Experimental results signpost that the proposed novel technique generates superior visual effects compared to other widely used undersea color image quality evaluation techniques.

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