Bachelor's of Engineering final year project. Completed 2020
-
Updated
Apr 16, 2023 - MATLAB
Bachelor's of Engineering final year project. Completed 2020
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.
Demos and tutorials using piQture and Qiskit
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.
Module for fast encoding and decoding of images into quantum states using the FRQI and NEQR method
Single image super resolution algorithm RED+ADMM+De-QuIP
Denoising by Quantum Interactive Patches
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.
Implementation of Image encoding in FRQI image model and reconstructing the image from the Quantum states,
piQture: A quantum machine learning library for image processing.
Add a description, image, and links to the quantum-image-processing topic page so that developers can more easily learn about it.
To associate your repository with the quantum-image-processing topic, visit your repo's landing page and select "manage topics."