From 623acbbc37fc5745064821b294d3da5ccf5c931c Mon Sep 17 00:00:00 2001
From: ShenZheng2000 <69662345+ShenZheng2000@users.noreply.github.com>
Date: Tue, 5 Oct 2021 23:47:21 +0800
Subject: [PATCH] Update README.md

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 # Semantic-Guided-Low-Light-Image-Enhancement
 This is the official Pytorch implementation for our paper "**Semantic-Guided Zero-Shot Learning for Low-Light Image/Video Enhancement.**" 
 
-A sample low-light video and its enhanced result is available at: [[Low-Light](https://www.youtube.com/watch?v=4Avy_xsczdU)]  [[Enhanced](https://www.youtube.com/watch?v=rN6Tf1E-kE8)]
+# Updates
+- 2021.10.5: A sample low-light video and its enhanced result is available at: [[Low-Light](https://www.youtube.com/watch?v=4Avy_xsczdU)]  [[Enhanced](https://www.youtube.com/watch?v=rN6Tf1E-kE8)]
+- 2021.10.5: My new [repository](https://github.com/ShenZheng2000/Awesome-Low-Light-Enhancement-with-Deep-Learning) contains a collection of low-light enhancement methods. Hope you will find it helpful. 
+- 2021.10.4: The arxiv link is available at http://arxiv.org/abs/2110.00970
 
-The arxiv link is available at http://arxiv.org/abs/2110.00970
 
 # Abstract
 Low-light images challenge both human perceptions and computer vision algorithms. It is crucial to make algorithms robust to enlighten low-light images for computational photography and computer vision applications such as real-time detection and segmentation tasks. This paper proposes a semantic-guided zero-shot low-light enhancement network which is trained in the absence of paired images, unpaired datasets, and segmentation annotation. Firstly, we design an efficient **enhancement factor extraction** network using depthwise separable convolution. Secondly, we propose a **recurrent image enhancement** network for progressively enhancing the low-light image. Finally, we introduce an **unsupervised semantic segmentation** network for preserving the semantic information. Extensive experiments on various benchmark datasets and a low-light video demonstrate that our model outperforms the previous state-of-the-art qualitatively and quantitatively. We further discuss the benefits of the proposed method for low-light detection and segmentation.