Search Results for author: Huihui Bai

Found 17 papers, 13 papers with code

You Can Mask More For Extremely Low-Bitrate Image Compression

1 code implementation27 Jun 2023 Anqi Li, Feng Li, Jiaxin Han, Huihui Bai, Runmin Cong, Chunjie Zhang, Meng Wang, Weisi Lin, Yao Zhao

Extensive experiments have demonstrated that our approach outperforms recent state-of-the-art methods in R-D performance, visual quality, and downstream applications, at very low bitrates.

Image Compression

Exploring Resolution Fields for Scalable Image Compression with Uncertainty Guidance

1 code implementation15 Jun 2023 Dongyi Zhang, Feng Li, Man Liu, Runmin Cong, Huihui Bai, Meng Wang, Yao Zhao

In this work, we explore the potential of resolution fields in scalable image compression and propose the reciprocal pyramid network (RPN) that fulfills the need for more adaptable and versatile compression.

Image Compression

Progressive Semantic-Visual Mutual Adaption for Generalized Zero-Shot Learning

1 code implementation CVPR 2023 Man Liu, Feng Li, Chunjie Zhang, Yunchao Wei, Huihui Bai, Yao Zhao

Generalized Zero-Shot Learning (GZSL) identifies unseen categories by knowledge transferred from the seen domain, relying on the intrinsic interactions between visual and semantic information.

Attribute Generalized Zero-Shot Learning

Bridging Component Learning with Degradation Modelling for Blind Image Super-Resolution

1 code implementation3 Dec 2022 Yixuan Wu, Feng Li, Huihui Bai, Weisi Lin, Runmin Cong, Yao Zhao

In this paper, we analyze the degradation of a high-resolution (HR) image from image intrinsic components according to a degradation-based formulation model.

Image Super-Resolution

Learning Deep Interleaved Networks with Asymmetric Co-Attention for Image Restoration

1 code implementation29 Oct 2020 Feng Li, Runmin Cong, Huihui Bai, Yifan He, Yao Zhao, Ce Zhu

In this paper, we present a deep interleaved network (DIN) that learns how information at different states should be combined for high-quality (HQ) images reconstruction.

Deblurring Image Deblurring +2

Deep Interleaved Network for Image Super-Resolution With Asymmetric Co-Attention

1 code implementation24 Apr 2020 Feng Li, Runming Cong, Huihui Bai, Yifan He

Recently, Convolutional Neural Networks (CNN) based image super-resolution (SR) have shown significant success in the literature.

Image Super-Resolution

Concurrently Extrapolating and Interpolating Networks for Continuous Model Generation

1 code implementation12 Jan 2020 Lijun Zhao, Jinjing Zhang, Fan Zhang, Anhong Wang, Huihui Bai, Yao Zhao

Most deep image smoothing operators are always trained repetitively when different explicit structure-texture pairs are employed as label images for each algorithm configured with different parameters.

image smoothing

Deep Optimized Multiple Description Image Coding via Scalar Quantization Learning

2 code implementations12 Jan 2020 Lijun Zhao, Huihui Bai, Anhong Wang, Yao Zhao

In this paper, we introduce a deep multiple description coding (MDC) framework optimized by minimizing multiple description (MD) compressive loss.

Quantization

Deep Multiple Description Coding by Learning Scalar Quantization

1 code implementation5 Nov 2018 Lijun Zhao, Huihui Bai, Anhong Wang, Yao Zhao

Secondly, two entropy estimation networks are learned to estimate the informative amounts of the quantized tensors, which can further supervise the learning of multiple description encoder network to represent the input image delicately.

Quantization

Virtual Codec Supervised Re-Sampling Network for Image Compression

1 code implementation22 Jun 2018 Lijun Zhao, Huihui Bai, Anhong Wang, Yao Zhao

In order to train RSN network and IDN network together in an end-to-end fashion, our VCN network intimates projection from the re-sampled vectors to the IDN-decoded image.

Dimensionality Reduction Image Compression +1

Mixed-Resolution Image Representation and Compression with Convolutional Neural Networks

no code implementations2 Feb 2018 Lijun Zhao, Huihui Bai, Feng Li, Anhong Wang, Yao Zhao

Firstly, given one input image, feature description neural network (FDNN) is used to generate a new representation of this image, so that this image representation can be more efficiently compressed by standard codec, as compared to the input image.

Image Compression Quantization

Multiple Description Convolutional Neural Networks for Image Compression

no code implementations20 Jan 2018 Lijun Zhao, Huihui Bai, Anhong Wang, Yao Zhao

Thirdly, multiple description virtual codec network (MDVCN) is proposed to bridge the gap between MDGN network and MDRN network in order to train an end-to-end MDC framework.

Image Compression

Learning a Virtual Codec Based on Deep Convolutional Neural Network to Compress Image

1 code implementation16 Dec 2017 Lijun Zhao, Huihui Bai, Anhong Wang, Yao Zhao

Due to the challenge of directly learning a non-linear function for a standard codec based on convolutional neural network, we propose to learn a virtual codec neural network to approximate the projection from the valid description image to the post-processed compressed image, so that the gradient could be efficiently back-propagated from the post-processing neural network to the feature description neural network during training.

Blocking Image Compression +2

Simultaneously Color-Depth Super-Resolution with Conditional Generative Adversarial Network

no code implementations30 Aug 2017 Lijun Zhao, Huihui Bai, Jie Liang, Bing Zeng, Anhong Wang, Yao Zhao

Firstly, given the low-resolution depth image and low-resolution color image, a generative network is proposed to leverage mutual information of color image and depth image to enhance each other in consideration of the geometry structural dependency of color-depth image in the same scene.

Edge Detection Generative Adversarial Network +5

Local Activity-tuned Image Filtering for Noise Removal and Image Smoothing

no code implementations9 Jul 2017 Lijun Zhao, Jie Liang, Huihui Bai, Lili Meng, Anhong Wang, Yao Zhao

Both frameworks employ the division of gradient and the local activity measurement to achieve noise removal.

Image Denoising image smoothing

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