- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
A Pipeline Neural Network For Low-Light Image Enhancement
摘要： Low-light image enhancement is an important challenge in computer vision. Most of low-light images taken in low-light conditions usually look noisy and dark, which makes it more difficult for subsequent computer vision tasks. In this paper, inspired by multi-scale retinex, we present a low-light image enhancement pipeline network based on an end-to-end fully convolutional networks and discrete wavelet transformation (DWT). Firstly, we show that Multi Scale Retinex (MSR) can be considered as a convolutional neural network (CNN) with Gaussian convolution kernel and blending the result of DWT can improve the image produced by MSR. Secondly, we propose our pipeline neural network, consisting of denoising net and low light image enhancement net (LLIE-net) which learns a function from a pair of dark and bright images. Finally, we evaluate our method both in synthetic dataset and public dataset. Experiments reveal that in comparison with other state-of-the-art methods, our methods achieve better performance in the perspective of qualitative and quantitative analysis.
关键词： Convolutional Neural Network,LLIE-Net,Low-light image enhancement
更新于2025-09-23 15:22:29
-
[IEEE 2018 Digital Image Computing: Techniques and Applications (DICTA) - Canberra, Australia (2018.12.10-2018.12.13)] 2018 Digital Image Computing: Techniques and Applications (DICTA) - Adversarial Context Aggregation Network for Low-Light Image Enhancement
摘要： Image captured in the low-light environments usually suffers from the low dynamic ranges and noise which degrade the quality of the image. Recently, convolutional neural network (CNN) has been employed for low-light image enhancement to simultaneously perform the brightness enhancement and noise removal. Although conventional CNN based techniques exhibit superior performance compared to traditional non-CNN based methods, they often produce the image with visual artifacts due to the small receptive field in their network. In order to cope with this problem, we propose an adversarial context aggregation network (ACA-net) for low-light image enhancement, which effectively aggregates the global context via full-resolution intermediate layers. In the proposed method, we first increase the brightness of a low-light image using the two different gamma correction functions and then feed the brightened images to CNN to obtain the enhanced image. To this end, we train ACA network using L1 pixel-wise reconstruction loss and adversarial loss which encourages the network to generate a natural image. Experimental results show that the proposed method achieves state-of-the-art results in terms of peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM).
关键词： context aggregation,Low-light image enhancement,Convolutional neural network,generative adversarial network
更新于2025-09-19 17:15:36