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Knowledge-guided Semantic Computing Network
Guangming Shi, Zhongqiang Zhang, Dahua Gao +4
It is very useful to integrate human knowledge and experience into traditional neural networks for faster learning speed, fewer training samples and better interpretability. Howeve…
Learning Hybrid Sparsity Prior for Image Restoration: Where Deep Learning Meets Sparse Coding
Fangfang Wu, Weisheng Dong, Guangming Shi +1
State-of-the-art approaches toward image restoration can be classified into model-based and learning-based. The former - best represented by sparse coding techniques - strive to ex…
The Shape of Sight: A Homological Framework for Unifying Visual Perception
Xin Li
Visual perception, the brain's construction of a stable world from sensory data, faces several long-standing, fundamental challenges. While often studied separately, these problems…
Full Image Recover for Block-Based Compressive Sensing
Xuemei Xie, Chenye Wang, Jiang Du +1
Recent years, compressive sensing (CS) has improved greatly for the application of deep learning technology. For convenience, the input image is usually measured and reconstructed…
Perceptual Compressive Sensing
Jiang Du, Xuemei Xie, Chenye Wang +1
Compressive sensing (CS) works to acquire measurements at sub-Nyquist rate and recover the scene images. Existing CS methods always recover the scene images in pixel level. This ca…
ConvCSNet: A Convolutional Compressive Sensing Framework Based on Deep Learning
Xiaotong Lu, Weisheng Dong, Peiyao Wang +2
Compressive sensing (CS), aiming to reconstruct an image/signal from a small set of random measurements has attracted considerable attentions in recent years. Due to the high dimen…
Denoising Prior Driven Deep Neural Network for Image Restoration
Weisheng Dong, Peiyao Wang, Wotao Yin +3
Deep neural networks (DNNs) have shown very promising results for various image restoration (IR) tasks. However, the design of network architectures remains a major challenging for…