Zero-Reference Deep Curve Estimation (Zero-DCE) is currently one of the most popular low-light image enhancement methods. Through extensive experimentation, we observe that: (i) the excellent performance of Zero-DCE depends on the training data with multiple exposure levels, (ii) it cannot effectively handle uneven light, extremely low light, or overexposed images in natural environments. Therefore, we propose an improved zero-reference dual-illumination deep curve estimation method for low-light image enhancement named Zero-DiDCE, which can enhance, suppress, or maintain light levels for images. The adaptive light enhancement curve was designed to handle images with different exposure levels. An iterator and amplitude controller are designed to control the curve enhancement intensity by calculating the gap between the input image and the optimal light level. Furthermore, instead of the DCE-Net in Zero-DCE only taking the input image as network input, our DiDCE-Net in Zero-DiDCE takes the input image and the inverted input image simultaneously as network input to ensure that the training set contains samples with multiple exposure levels. A piecewise non-reference loss function is designed to guide the training of DiDCE-Net from the perspective of information loss. Qualitative and quantitative experiments show that our method can handle images with different levels of exposure well and outperforms state-of-the-art methods. In addition, the proposed curve and iterator can be integrated into other methods to improve their enhancement effects. The code is available at https://github.com/Wenhui-Luo/Zero-DiDCE.

Photorealistic style transfer is a challenging task, which demands the stylized image remains real. Existing methods are still suffering from unrealistic artifacts and heavy computational cost. In this paper, we propose a novel Style-Corpus Constrained Learning (SCCL) scheme to address these issues. The style-corpus with the style-specific and style-agnostic characteristics simultaneously is proposed to constrain the stylized image with the style consistency among different samples, which improves photorealism of stylization output. By using adversarial distillation learning strategy, a simple fast-to-execute network is trained to substitute previous complex feature transforms models, which reduces the computational cost significantly. Experiments demonstrate that our method produces rich-detailed photorealistic images, with 13 ~ 50 times faster than the state-of-the-art method (WCT ² ).

To improve the accuracy of human pose estimation, a novel method based on the deep high-resolution network (HRNet) and equipped with double attention residual blocks is proposed. Firstly, the channel attention and spatial attention modules are added to the residual block of feature extraction, resulting in the network paying more attention to the target area which needs to be extracted important information and suppressed unimportant information. Moreover, this paper proposes a novel module, Parallel Residual Attention Block (PRAB), which parallels the 3x3 group convolution of ResNeXt to the 3x3 convolution layer in the Bottleneck of ResNet, and then adds channel attention and spatial attention modules to these two branches respectively. In this way, the network can further improve the accuracy of human keypoint detection without significantly increasing the computation overhead. To demonstrate the effectiveness of our method, a series of comparative experiments are conducted on the MPII Human Pose dataset and the COCO2017 keypoint detection dataset. Experimental results illustrate that the attention mechanism is effective to improve the accuracy of human pose estimation and the proposed PRAB obtained the best results 90.5% on MPII which outperforms the existing methods.