VLG Summer Project

"# Extreme-Low-Light-Image-Denoising-NTIRE-Challenge-2k24"

======= "# Extreme-Low-Light-Image-Denoising-NTIRE-Challenge-2k24"/

Details on PSNR mentioned in the final report. Low-Light Image Denoiser This repository contains the implementation of a low-light image enhancement model that was used in the NTIRE-2024 Low Light Image Enhancement competition.

Model Overview This model is based on the ImageLab architecture, specifically tailored for low-light image enhancement. This architecture integrates several techniques to improve the image quality:

Spatial Information Enhancement: Improves the detail and structure of low-light images. Intricate Feature Capture: Effectively captures detailed features in low-light conditions. Multi-Scale Feature Refinement: Refines features at multiple scales for better image quality. Effective Noise Suppression: Reduces noise while preserving important image details. Performance The enhanced images produced by this model achieved an average Peak Signal-to-Noise Ratio (PSNR) value of 24 dB on the training set.

Training Details Training Platform: Kaggle Notebook GPU Used: NVIDIA Tesla P100 Reference The model architecture and training methodology are based on the research presented in the following paper: "# drone-fault-detection"