Subrata Bhattacharjee
Papers
1
Total Citations
3
H-Index
1
About
Subrata Bhattacharjee is a researcher at the forefront of computational imaging and medical image analysis, with a particular focus on enhancing surgical visualization through deep learning. His key research areas include super-resolution reconstruction, attention-based neural architectures, and endoscopic image enhancement. Bhattacharjee's major contribution is the development of HAMSRNet—a Hybrid Attention Multiscale Super-Resolution Network designed specifically for endoscopic images. This work addresses a critical challenge in minimally invasive surgery: the low resolution, noise, and blur inherent in stereo endoscopy cameras, which can obscure small anatomical structures. By integrating hybrid attention mechanisms with multiscale feature extraction, his network significantly improves image clarity, thereby enhancing surgeons' depth perception and situational awareness. Although his most-cited paper has garnered 3 citations since 2025, the novelty and clinical relevance of his approach position it as a promising foundation for future advancements in surgical vision. Bhattacharjee's work exemplifies how targeted AI solutions can bridge the gap between hardware limitations and the high-fidelity visual demands of modern medicine.
Research Focus
Key Achievements
Top Papers
- 1