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- 1,046
- Group-Based Sparse Representation for Image Restoration768
The proposed group-based sparse representation (GSR) is able to sparsely represent natural images in the domain of group, which enforces the intrinsic local sparsity and nonlocal self-similarity of images simultaneously in a unified framework.
- 440
- Learning Convolutional Networks for Content-Weighted Image Compression421
The bit rate of the different parts of the image is adapted to local content, and the content-aware bit rate is allocated under the guidance of a content-weighted importance map so that the sum of the importance map can serve as a continuous alternative of discrete entropy estimation to control compression rate.
- 416
- Fast and robust text detection in images and video frames380
A novel coarse-to-fine algorithm that is able to locate text lines even under complex background is proposed and Experimental results show that this approach can fast and robustly detect text lines under various conditions.
- 274
- An End-to-End Compression Framework Based on Convolutional Neural Networks232
Experimental results validate that the proposed compression framework greatly outperforms several compression frameworks that use existing image coding standards with the state-of-the-art deblocking or denoising post-processing methods.
- Image Restoration Using Joint Statistical Modeling in a Space-Transform Domain191
A joint statistical modeling (JSM) in an adaptive hybrid space-transform domain is established, which offers a powerful mechanism of combining local smoothness and nonlocal self-similarity simultaneously to ensure a more reliable and robust estimation.
- 188
- Image compressive sensing recovery using adaptively learned sparsifying basis via L0 minimization181
This paper proposes a new framework for image compressive sensing recovery using adaptively learned sparsifying basis via L0 minimization, and proposes a split Bregman iteration based technique to solve the non-convex L 0 minimization problem efficiently.
- A Chinese sign language recognition system based on SOFM/SRN/HMM173
This paper uses the self-organizing feature maps (SOFM) as different signers' feature extractor for continuous hidden Markov models (HMM) so as to transform input signs into significant and low-dimensional representations that can be well modeled by the emission probabilities of HMM.
- Deep networks for compressed image sensing165
A deep network is proposed to recover the image, which imitates traditional compressed sensing reconstruction processes, and offers a very significant quality improvement compared against state-of-the-art ones.
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- Object detection using spatial histogram features125
This paper employs Fisher criterion and mutual information to measure discriminability and features correlation of spatial histogram features, and trains a hierarchical classifier by combining cascade histogram matching and support vector machine.
- 119
- Joint video/depth rate allocation for 3D video coding based on view synthesis distortion model116
Experimental results show that the proposed distortion model is capable of approximately estimating the actual distortion for the synthesized view, and that the suggested rate allocation method can almost achieve the identical rate allocation performance as the full-search method at less computational cost.
- 112
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-14. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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