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,
Signal and image denoising using quantum adaptive transformation.
Plug-and-Play ADMM scheme based on an adaptive denoiser using the Schroedinger equation's solutions of quantum physics.
Quantum Annealing-based Unsupervised Segmentation Algorithm
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
Variational Quantum Algorithms for Unsupervised Image Segmentation
Denoising by Quantum Interactive Patches
Single image super resolution algorithm RED+ADMM+De-QuIP
Q-SupCon integrates quantum principles into supervised contrastive learning, enhancing feature learning with minimal labeled data for efficient image classification, especially in medical applications.
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.
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