The project uses Generative Adversarial Networks (GANs) to convert grayscale Synthetic Aperture Radar (SAR) images into visually meaningful RGB images, improving interpretability for analysis, surveillance, and disaster management.
Cell-segmentation using time-sequence data, through the adaptation of a U-TAE Model.
U-Net deep learning for cardiac MRI segmentation and 12-class disease classification. Built with Flask & TensorFlow.
A two-stage deepfake detection system that combines Retinex-guided U-Net image enhancement with ResNet50-based classification, deployed via a Flask web application.
Classifier-free guided diffusion model with U-Net architecture for digit image synthesis.
A U-Net based CNN autoencoder designed to denoise noisy images before classification, improving input quality and boosting overall model accuracy.
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