D. Kavitha
Papers
2
Total Citations
23
H-Index
2
About
D. Kavitha is a researcher specializing in computer vision and image processing, with a focus on enhancing visual data in challenging environments. Her primary contributions lie in underwater image restoration and natural scene text detection. In her most cited work, "CNN based color balancing and denoising technique for underwater images: CNN-CBDT" (2023, 20 citations), she addresses the critical issues of color distortion and blurring caused by light scattering in underwater environments. This deep learning approach offers a robust solution for improving image quality in marine research and underwater exploration. Additionally, her paper "Text Detection Based on Text Shape Feature Analysis with Intelligent Grouping in Natural Scene Images" (2020) explores innovative methods for extracting text from complex, real-world images, contributing to advancements in automated document analysis and augmented reality. Kavitha’s work demonstrates a commitment to solving practical problems in image degradation and pattern recognition, with her CNN-CBDT technique standing out as a notable achievement for its potential applications in environmental monitoring and underwater robotics. Her research continues to inspire further developments in adaptive image enhancement technologies.
Research Focus
Key Achievements
Top Papers
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- 2