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This project is aimed to develop powerful and lightweight networks to address the two tasks presented in the NTIRE 2017 SuperResolution Challenge. The two deep learning models proposed, i.e. a CNN and a GAN, achieved satisfactory results demonstrating an important ability in introducing high-frequency information to the original low resolution i…
Quantum Single Image Super-Resolution Template. This algorithm demonstrates formulating quantum SISR as a sparse coding optimization problem, which is solved using the Dynex Neuromorphic Quantum Computing Platform via the Dynex SDK. This AQC-based algorithm is demonstrated to achieve improved SISR accuracy.
This repository is about my experiences and experiments on the single image super resolution task, which is about retrievaling a high resolution image from a low resolution image using deep learning.
Implementation of EDSR, ESPCN, LAPSRN, SRCNN, SRGAN and WDSR for single image super-resolution (SISR) based on Tensorflow 2.x for CMU's 10-707 Advanced Deep Learning Final Project
Explore Super Image Resolution through sophisticated degradation modeling. Explore this approach to enhance real image quality and elevate your image enhancement capabilities
This repository stores scripts used to run COMASure and its extensions. The models are studied as part of the requirements for the MSc Data Science and Machine Learning dissertation at UCL.
PyTorch super-resolution model (OverNet) with RGB support and ONNX exporter (OverNet: Lightweight Multi-Scale Super-Resolution with Overscaling Network (WACV 2021))
Comparative study of lightweight generator models (ESPCN, FSRCNN, IDN) in the SRGAN framework for Single Image Super-Resolution (SISR). Explore the trade-offs between performance and efficiency in GAN-based SISR.