Search Results for author: Qingmin Liao

Found 14 papers, 5 papers with code

ER-IQA: Boosting Perceptual Quality Assessment Using External Reference Images

no code implementations6 May 2021 Jingyu Guo, Wei Wang, Wenming Yang, Qingmin Liao, Jie zhou

In this paper, we introduce a brand new scheme, namely external-reference image quality assessment (ER-IQA), by introducing external reference images to bridge the gap between FR and NR-IQA.

Image Quality Assessment

SCNet: Enhancing Few-Shot Semantic Segmentation by Self-Contrastive Background Prototypes

no code implementations19 Apr 2021 Jiacheng Chen, Bin-Bin Gao, Zongqing Lu, Jing-Hao Xue, Chengjie Wang, Qingmin Liao

To this end, we generate self-contrastive background prototypes directly from the query image, with which we enable the construction of complete sample pairs and thus a complementary and auxiliary segmentation task to achieve the training of a better segmentation model.

Few-Shot Semantic Segmentation Metric Learning +1

A region-based descriptor network for uniformly sampled keypoints

no code implementations26 Jan 2021 Kai Lv, Zongqing Lu, Qingmin Liao

By the new descriptor, we can obtain more high confidence matching points without extremum operation.

Attention Cube Network for Image Restoration

1 code implementation13 Sep 2020 Yucheng Hang, Qingmin Liao, Wenming Yang, Yupeng Chen, Jie zhou

The adaptive spatial attention branch (ASAB) and the adaptive channel attention branch (ACAB) constitute the adaptive dual attention module (ADAM), which can capture the long-range spatial and channel-wise contextual information to expand the receptive field and distinguish different types of information for more effective feature representations.

Image Restoration

Real-MFF: A Large Realistic Multi-focus Image Dataset with Ground Truth

no code implementations28 Mar 2020 Juncheng Zhang, Qingmin Liao, Shaojun Liu, Haoyu Ma, Wenming Yang, Jing-Hao Xue

In this letter, we introduce a large and realistic multi-focus dataset called Real-MFF, which contains 710 pairs of source images with corresponding ground truth images.

XSepConv: Extremely Separated Convolution

no code implementations27 Feb 2020 Jiarong Chen, Zongqing Lu, Jing-Hao Xue, Qingmin Liao

Depthwise convolution has gradually become an indispensable operation for modern efficient neural networks and larger kernel sizes ($\ge5$) have been applied to it recently.

An α-Matte Boundary Defocus Model Based Cascaded Network for Multi-focus Image Fusion

2 code implementations29 Oct 2019 Haoyu Ma, Qingmin Liao, Juncheng Zhang, Shaojun Liu, Jing-Hao Xue

Based on this {\alpha}-matte defocus model and the generated data, a cascaded boundary aware convolutional network termed MMF-Net is proposed and trained, aiming to achieve clearer fusion results around the FDB.

LCSCNet: Linear Compressing Based Skip-Connecting Network for Image Super-Resolution

1 code implementation9 Sep 2019 Wenming Yang, Xuechen Zhang, Yapeng Tian, Wei Wang, Jing-Hao Xue, Qingmin Liao

In this paper, we develop a concise but efficient network architecture called linear compressing based skip-connecting network (LCSCNet) for image super-resolution.

Image Super-Resolution

Boundary Aware Multi-Focus Image Fusion Using Deep Neural Network

no code implementations30 Mar 2019 Haoyu Ma, Juncheng Zhang, Shaojun Liu, Qingmin Liao

Since it is usually difficult to capture an all-in-focus image of a 3D scene directly, various multi-focus image fusion methods are employed to generate it from several images focusing at different depths.

Lightweight Feature Fusion Network for Single Image Super-Resolution

2 code implementations15 Feb 2019 Wenming Yang, Wei Wang, Xuechen Zhang, Shuifa Sun, Qingmin Liao

Specifically, a spindle block is composed of a dimension extension unit, a feature exploration unit and a feature refinement unit.

Image Super-Resolution

Optical Flow Super-Resolution Based on Image Guidence Using Convolutional Neural Network

no code implementations3 Sep 2018 Liping Zhang, Zongqing Lu, Qingmin Liao

With the motivation of various convolutional neural network(CNN) structures succeeded in single image super-resolution(SISR) task, an end-to-end convolutional neural network is proposed to reconstruct the high resolution(HR) optical flow field from initial LR optical flow with the guidence of the first frame used in optical flow estimation.

Image Super-Resolution Optical Flow Estimation

Cannot find the paper you are looking for? You can Submit a new open access paper.