Research PROJECTS

Light Field Microscopy

Enhancing 3D point cloud reconstruction for light field microscopy using deep learning-based convolutional neural networks. This work focuses on improving the depth, 3D resolution and visualization of microscopic samples.

Identification and correction of microlens-array error in an integral-imaging-microscopy system

Depth estimation for integral imaging microscopy using a 3D–2D CNN with a weighted median filter

Enhanced 3D Point Cloud Reconstruction for Light Field Microscopy Using U-Net-based Convolutional Neural Networks

High-quality 3D visualization system for light-field microscopy with fine-scale shape measurement through accurate 3D surface data

Medical Image Analysis

Creating smart IoT models for dermatological lesion diagnosis using adaptive segmentation and improved EfficientNet architectures. Also working on MRI reconstruction techniques to correct motion artifacts using Swin Transformers.

Squeeze-MNet: Precise skin cancer detection model for low computing IoT devices using transfer learning

A Hybrid Residual Attention Convolutional Neural Network for Compressed Sensing Magnetic Resonance Image Reconstruction

Holographic 3D Display

Developing advanced deep learning models for direct phase-only hologram generation using complex-valued neural networks. This research aims to improve the quality and speed of computer-generated holography for next-generation 3D displays.

Advanced deep learning model for direct phase-only hologram generation using complex-valued neural networks.

Photograph of the experimental setup.

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