piQture: A quantum machine learning library for image processing.

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

Quantum Annealing-based Unsupervised Segmentation Algorithm

Signal and image denoising using quantum adaptive transformation.

Variational Quantum Algorithms for Unsupervised Image Segmentation

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

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.

Bachelor's of Engineering final year project. Completed 2020

Denoising by Quantum Interactive Patches

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

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

Demos and tutorials using piQture and Qiskit

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

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

An implementation of qubit-efficient encoding strategies for image segmentation in pennylane.