57 citations · 102 across the 11 of their papers we have counts for
10 papers · 1 filter
Bridging Component Learning with Degradation Modelling for Blind Image Super-Resolution
Yixuan Wu, Feng Li, Huihui Bai +3
Convolutional Neural Network (CNN)-based image super-resolution (SR) has exhibited impressive success on known degraded low-resolution (LR) images. However, this type of approach i…
Learning Detail-Structure Alternative Optimization for Blind Super-Resolution
Feng Li, Yixuan Wu, Huihui Bai +3
Existing convolutional neural networks (CNN) based image super-resolution (SR) methods have achieved impressive performance on bicubic kernel, which is not valid to handle unknown…
Towards Fast and Accurate Real-World Depth Super-Resolution: Benchmark Dataset and Baseline
Lingzhi He, Hongguang Zhu, Feng Li +6
Depth maps obtained by commercial depth sensors are always in low-resolution, making it difficult to be used in various computer vision tasks. Thus, depth map super-resolution (SR)…
Learning Deep Interleaved Networks with Asymmetric Co-Attention for Image Restoration
Feng Li, Runmin Cong, Huihui Bai +3
Recently, convolutional neural network (CNN) has demonstrated significant success for image restoration (IR) tasks (e.g., image super-resolution, image deblurring, rain streak remo…
Deep Interleaved Network for Image Super-Resolution With Asymmetric Co-Attention
Feng Li, Runming Cong, Huihui Bai +1
Recently, Convolutional Neural Networks (CNN) based image super-resolution (SR) have shown significant success in the literature. However, these methods are implemented as single-p…
Deep Optimized Multiple Description Image Coding via Scalar Quantization Learning
Lijun Zhao, Huihui Bai, Anhong Wang +1
In this paper, we introduce a deep multiple description coding (MDC) framework optimized by minimizing multiple description (MD) compressive loss. First, MD multi-scale-dilated enc…