Jiahao Cui

Jiahao Cui, Wei Jiang, Zhan Peng et al.

High dynamic range (HDR) video rendering from low dynamic range (LDR) videos where frames are of alternate exposure encounters significant challenges, due to the exposure change and absence at each time stamp. The exposure change and absence make existing methods generate flickering HDR results. In this paper, we propose a novel paradigm to render HDR frames via completing the absent exposure information, hence the exposure information is complete and consistent. Our approach involves interpolating neighbor LDR frames in the time dimension to reconstruct LDR frames for the absent exposures. Combining the interpolated and given LDR frames, the complete set of exposure information is available at each time stamp. This benefits the fusing process for HDR results, reducing noise and ghosting artifacts therefore improving temporal consistency. Extensive experimental evaluations on standard benchmarks demonstrate that our method achieves state-of-the-art performance, highlighting the importance of absent exposure completing in HDR video rendering. The code is available at https://github.com/cuijiahao666/NECHDR.

Jiahao Cui, Ruoxin Xiao, Shiyuan Fang et al.

Liver tumor segmentation in CT images is a critical step in the diagnosis, surgical planning and postoperative evaluation of liver disease. An automatic liver and tumor segmentation method can greatly relieve physicians of the heavy workload of examining CT images and better improve the accuracy of diagnosis. In the last few decades, many modifications based on U-Net model have been proposed in the literature. However, there are relatively few improvements for the advanced UNet++ model. In our paper, we propose an encoding feature supervised UNet++(ES-UNet++) and apply it to the liver and tumor segmentation. ES-UNet++ consists of an encoding UNet++ and a segmentation UNet++. The well-trained encoding UNet++ can extract the encoding features of label map which are used to additionally supervise the segmentation UNet++. By adding supervision to the each encoder of segmentation UNet++, U-Nets of different depths that constitute UNet++ outperform the original version by average 5.7% in dice score and the overall dice score is thus improved by 2.1%. ES-UNet++ is evaluated with dataset LiTS, achieving 95.6% for liver segmentation and 67.4% for tumor segmentation in dice score. In this paper, we also concluded some valuable properties of ES-UNet++ by conducting comparative anaylsis between ES-UNet++ and UNet++:(1) encoding feature supervision can accelerate the convergence of the model.(2) encoding feature supervision enhances the effect of model pruning by achieving huge speedup while providing pruned models with fairly good performance.

Wei Jiang, Jiahao Cui, Yizheng Wu et al.

Reconstructing high dynamic range (HDR) images from low dynamic range (LDR) bursts plays an essential role in the computational photography. Impressive progress has been achieved by learning-based algorithms which require LDR-HDR image pairs. However, these pairs are hard to obtain, which motivates researchers to delve into the problem of annotation-efficient HDR image reconstructing: how to achieve comparable performance with limited HDR ground truths (GTs). This work attempts to address this problem from the view of semi-supervised learning where a teacher model generates pseudo HDR GTs for the LDR samples without GTs and a student model learns from pseudo GTs. Nevertheless, the confirmation bias, i.e., the student may learn from the artifacts in pseudo HDR GTs, presents an impediment. To remove this impediment, an uncertainty-based masking process is proposed to discard unreliable parts of pseudo GTs at both pixel and patch levels, then the trusted areas can be learned from by the student. With this novel masking process, our semi-supervised HDR reconstructing method not only outperforms previous annotation-efficient algorithms, but also achieves comparable performance with up-to-date fully-supervised methods by using only 6.7% HDR GTs.