Related papers: FLOL: Fast Baselines for Real-World Low-Light Enhancement

FLOL: Fast Baselines for Real-World Low-Light Enhancement

URL: http://arxiv.org/abs/2501.09718v1
Date: Thu, 16 Jan 2025 18:06:09 GMT
Title: FLOL: Fast Baselines for Real-World Low-Light Enhancement
Authors: Juan C. Benito, Daniel Feijoo, Alvaro Garcia, Marcos V. Conde,
Abstract summary: Low-Light Image Enhancement (LLIE) is a key task in computational photography and imaging. We propose a lightweight neural network that combines image processing in the frequency and spatial domains. Our method, FLOL+, is one of the fastest models for this task, achieving state-of-the-art results on popular real scenes datasets.
Score: 6.646501936980895
License:
Abstract: Low-Light Image Enhancement (LLIE) is a key task in computational photography and imaging. The problem of enhancing images captured during night or in dark environments has been well-studied in the image signal processing literature. However, current deep learning-based solutions struggle with efficiency and robustness in real-world scenarios (e.g. scenes with noise, saturated pixels, bad illumination). We propose a lightweight neural network that combines image processing in the frequency and spatial domains. Our method, FLOL+, is one of the fastest models for this task, achieving state-of-the-art results on popular real scenes datasets such as LOL and LSRW. Moreover, we are able to process 1080p images under 12ms. Code and models at https://github.com/cidautai/FLOL

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