GLADNet: Low-Light Enhancement Network with Global Awareness
FG, 2018 | PDF
* indicates equal contributions
We address the problem of low-light enhancement. Our key idea is to first calculate a global illumination estimation for the low-light input, then adjust the illumination under the guidance of the estimation and supplement the details using a concatenation with the original input. Considering that, we propose a GLobal illumination-Aware and Detail-preserving Network (GLADNet). The input image is rescaled to a certain size and then put into an encoder-decoder network to generate global priori knowledge of the illumination. Based on the global prior and the original input image, a convolutional network is employed for detail reconstruction. For training GLADNet, we use a synthetic dataset generated from RAW images. Extensive experiments demonstrate the superiority of our method over other com- pared methods on the real low-light images captured in various conditions.
The architecture of GLADNet. The architecture consists of two steps, global illumination estimation step and detail reconstruction step. In the first step, the encoder-decoder network produces an illumination estimation of a fixed size (96 × 96 here). In the second step, a convolutional network utilizes the input image and the outputs from the previous step to compensate the details.
The proposed method is implemented by Tensorflow on NVIDIA GeForce GTX 1080. We compare the proposed method with MSRCR, LIME, DeHZ, and SRIE and evaluate the results on public LIME-data, DICM, and MEF datasets.
We use the Naturalness Image Quality Evaluator (NIQE)  no-reference image quality score for quantitative comparison. NIQE compares images to a default model computed from images of natural scenes. A smaller score indicates better perceptual quality. As shown in the table, our method outperforms other state-of-the-art methods on average.
For MSRCR, LIME, DeHZ, and SRIE, we used the MATLAB code provided by Zhenqiang Ying on lowlight. For these and GLADNet CPU version, we use Intel Core i5 at 2.7GHz. For GLADNet GPU version, we use Intel Core i7-6850K at 3.60GHz and NVIDIA GeForce GTX 1080. We calculated the average running time per image to enhance the MEF dataset.
Applications on Computer Vision:
We test several real low-light images and their corresponding enhanced results on Google Cloud Vision API. GLADNet helps Google Cloud Vision API identify the objects in this image.
Results of Google Cloud Vision API for “Eiffel Tower” from MEF dataset. Before enhancement, Google Cloud Vision can not recognize the Eiffel Tower. After enhanced by GLADNet, the Eiffel Tower is identified and marked by a green box.
Results for “Room” from LIME-data dataset. Potted plant and painting in the non-enhanced version are not identified by Google Cloud Vision.
Trained model: Github
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