quscope.image_processing.image_denoising
Quantum-Classical Denoising for Images
This module implements a hybrid quantum-classical framework for denoising noisy microscopy images, using quantum-enhanced image segmentation and adaptive classical filtering. Quantum confidence and entropy maps dervived from Grover’s search and frequency analysis guide the application of filtering techniques such as Gaussian, median, and Wiener filters.
The system works patch-wise, encoding image data into quantum circuits, extracting quantum features from measurements, and reconstructing a denoised image by selectively applying classical filters based on quantum metadata.
Key Features
Patch-based quantum feature extraction using Grover’s algorithm and QFT
Measurement-derived entropy, confidence, and spatial correlation
Adaptive denoising strategies guided by quantum-derived maps
Visualization of denoising performance and feature maps
Denoising performance metrics (SNR, edge preservation)
Classes
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Initialize the image denoiser. |