Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
Chunle Guo,Chongyi Li,Jichang Guo,Chen Change Loy,Junhui Hou,Sam Kwong,Runmin Cong +6 more
- pp 1780-1789
TLDR
A novel method, Zero-Reference Deep Curve Estimation (Zero-DCE), which formulates light enhancement as a task of image-specific curve estimation with a deep network and shows that it generalizes well to diverse lighting conditions.Abstract:
The paper presents a novel method, Zero-Reference Deep Curve Estimation (Zero-DCE), which formulates light enhancement as a task of image-specific curve estimation with a deep network. Our method trains a lightweight deep network, DCE-Net, to estimate pixel-wise and high-order curves for dynamic range adjustment of a given image. The curve estimation is specially designed, considering pixel value range, monotonicity, and differentiability. Zero-DCE is appealing in its relaxed assumption on reference images, i.e., it does not require any paired or unpaired data during training. This is achieved through a set of carefully formulated non-reference loss functions, which implicitly measure the enhancement quality and drive the learning of the network. Our method is efficient as image enhancement can be achieved by an intuitive and simple nonlinear curve mapping. Despite its simplicity, we show that it generalizes well to diverse lighting conditions. Extensive experiments on various benchmarks demonstrate the advantages of our method over state-of-the-art methods qualitatively and quantitatively. Furthermore, the potential benefits of our Zero-DCE to face detection in the dark are discussed.read more
Citations
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Journal ArticleDOI
Underwater Image Enhancement via Medium Transmission-Guided Multi-Color Space Embedding
TL;DR: Li et al. as mentioned in this paper proposed an underwater image enhancement network via medium transmission-guided multi-color space embedding, which enriches the diversity of feature representations by incorporating the characteristics of different color spaces into a unified structure.
Proceedings ArticleDOI
Calibrated RGB-D Salient Object Detection
Wei Ji,Jingjing Li,Shuang Yu,Miao Zhang,Yongri Piao,Shunyu Yao,Qi Bi,Kai Ma,Yefeng Zheng,Huchuan Lu,Li Cheng +10 more
TL;DR: Depth Calibration and Fusion (DCF) as mentioned in this paper proposes a learning strategy to calibrate the latent bias in the original depth maps towards boosting the SOD performance, and a simple yet effective cross reference module to fuse features from both RGB and depth modalities.
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Retinex-inspired Unrolling with Cooperative Prior Architecture Search for Low-light Image Enhancement
TL;DR: Building upon Retinex rule, RUAS first establishes models to characterize the intrinsic underexposed structure of low-light images and unroll their optimization processes to construct the authors' holistic propagation structure and is able to obtain a top-performing image enhancement network, which is with fast speed and requires few computational resources.
Journal ArticleDOI
Underwater Image Enhancement via Medium Transmission-Guided Multi-Color Space Embedding.
TL;DR: In this article, a multi-color space encoder network is proposed to enhance the diversity of feature representations by incorporating the characteristics of different color spaces into a unified structure, and the most discriminative features extracted from multiple color spaces are adaptively integrated and highlighted.
Journal ArticleDOI
Underwater image enhancement with global–local networks and compressed-histogram equalization
Xueyang Fu,Xiangyong Cao +1 more
TL;DR: This work proposes a two-branch network to compensate the global distorted color and local reduced contrast, respectively, and designs a compressed-histogram equalization to complement the data-driven deep learning, in which the parameters are fixed after training.
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