189 citations · 617 across the 16 of their papers we have counts for
12 papers · 1 filter
Optimization-Inspired Cross-Attention Transformer for Compressive Sensing
Jiechong Song, Chong Mou, Shiqi Wang +2
By integrating certain optimization solvers with deep neural networks, deep unfolding network (DUN) with good interpretability and high performance has attracted growing attention…
Panini-Net: GAN Prior Based Degradation-Aware Feature Interpolation for Face Restoration
Yinhuai Wang, Yujie Hu, Jian Zhang
Emerging high-quality face restoration (FR) methods often utilize pre-trained GAN models (\textit{i.e.}, StyleGAN2) as GAN Prior. However, these methods usually struggle to balance…
R-DFCIL: Relation-Guided Representation Learning for Data-Free Class Incremental Learning
Qiankun Gao, Chen Zhao, Bernard Ghanem +1
Class-Incremental Learning (CIL) struggles with catastrophic forgetting when learning new knowledge, and Data-Free CIL (DFCIL) is even more challenging without access to the traini…
Thousand to One: Semantic Prior Modeling for Conceptual Coding
Jianhui Chang, Zhenghui Zhao, Lingbo Yang +3
Conceptual coding has been an emerging research topic recently, which encodes natural images into disentangled conceptual representations for compression. However, the compression…
COLA-Net: Collaborative Attention Network for Image Restoration
Chong Mou, Jian Zhang, Xiaopeng Fan +2
Local and non-local attention-based methods have been well studied in various image restoration tasks while leading to promising performance. However, most of the existing methods…
Conceptual Compression via Deep Structure and Texture Synthesis
Jianhui Chang, Zhenghui Zhao, Chuanmin Jia +5
Existing compression methods typically focus on the removal of signal-level redundancies, while the potential and versatility of decomposing visual data into compact conceptual com…
Group-based Sparse Representation for Image Restoration
Jian Zhang, Debin Zhao, Wen Gao
Traditional patch-based sparse representation modeling of natural images usually suffer from two problems. First, it has to solve a large-scale optimization problem with high compu…
Image Compressive Sensing Recovery Using Adaptively Learned Sparsifying Basis via L0 Minimization
Jian Zhang, Chen Zhao, Debin Zhao +1
From many fewer acquired measurements than suggested by the Nyquist sampling theory, compressive sensing (CS) theory demonstrates that, a signal can be reconstructed with high prob…
Structural Group Sparse Representation for Image Compressive Sensing Recovery
Jian Zhang, Debin Zhao, Feng Jiang +1
Compressive Sensing (CS) theory shows that a signal can be decoded from many fewer measurements than suggested by the Nyquist sampling theory, when the signal is sparse in some dom…
Spatially Directional Predictive Coding for Block-based Compressive Sensing of Natural Images
Jian Zhang, Debin Zhao, Feng Jiang
A novel coding strategy for block-based compressive sens-ing named spatially directional predictive coding (SDPC) is proposed, which efficiently utilizes the intrinsic spatial cor-…
Image Super-Resolution via Dual-Dictionary Learning And Sparse Representation
Jian Zhang, Chen Zhao, Ruiqin Xiong +2
Learning-based image super-resolution aims to reconstruct high-frequency (HF) details from the prior model trained by a set of high- and low-resolution image patches. In this paper…
Improved Total Variation based Image Compressive Sensing Recovery by Nonlocal Regularization
Jian Zhang, Shaohui Liu, Debin Zhao +2
Recently, total variation (TV) based minimization algorithms have achieved great success in compressive sensing (CS) recovery for natural images due to its virtue of preserving edg…