Implementation of Image encoding in FRQI image model and reconstructing the image from the Quantum states,

Plug-and-Play ADMM scheme based on an adaptive denoiser using the Schroedinger equation's solutions of quantum physics.

Signal and image denoising using quantum adaptive transformation.

Denoising by Quantum Interactive Patches

Bachelor's of Engineering final year project. Completed 2020

Single image super resolution algorithm RED+ADMM+De-QuIP

Deep Denoising by Quantum Interactive Patches. A deep neural network called DIVA unfolding a baseline adaptive denoising algorithm (De-QuIP), relying on the theory of quantum many-body physics.

By training and employing a machine learning model that identifies and corrects the noise in quantum processed images, this model can compensate for the noisiness caused by the machine and retrieve a processing result similar to that performed by a classical computer with higher efficiency

Q-SupCon integrates quantum principles into supervised contrastive learning, enhancing feature learning with minimal labeled data for efficient image classification, especially in medical applications.

Module for fast encoding and decoding of images into quantum states using the FRQI and NEQR method