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Channel Attention and Multi-level Features Fusion for Single image Super-Resolution 33

Channel Attention and Multi-level Features Fusion for Single...

引用

33rd IEEE International conference on visual communications and image processing (IEEE VCIP)

作者： Lu, Yue Zhou, Yun Jiang, Zhuqing Guo, Xiaoqiang Yang, Zixuan Beijing Univ Posts & Telecommun Beijing Peoples R China Acad Broadcasting Sci Beijing Peoples R China

ISBN: (纸本)9781538644584

Convolutional neural networks (CNNs) have demonstrated superior performance in super-resolution (SR). However, most CNN-based SR methods neglect the different importance among feature channels or fail to take full advantage of the hierarchical features. To address these issues, this paper presents a novel recursive unit. Firstly, at the beginning of each unit, we adopt a compact channel attention mechanism to adaptively recalibrate the channel importance of input features. Then, the multi-level features, rather than only deep-level features, are extracted and fused. Additionally, we find that it will force our model to learn more details by using the learnable upsampling method (i.e., transposed convolution) only on residual branch (instead of using it both on residual branch and identity branch) while using the bicubic interpolation on the other branch. Analytic experiments show that our method achieves competitive results compared with the state-of-the-art methods and maintains faster speed as well.

关键词： Super-Resolution Convolutional Neural Networks Recursive Unit Channel Attention Multi-level Features Fusion

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Multiple Residual Learning Network for Single image Super-Resolution 33

Multiple Residual Learning Network for Single Image Super-Re...

引用

33rd IEEE International conference on visual communications and image processing (IEEE VCIP)

作者： Liu, Renhe Li, Sumei Hou, Chunping Lei, Guoqing Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China

ISBN: (纸本)9781538644584

Deep residual convolutional neural network (CNN) has recently achieved great success in image super-resolution (SR). Because residual learning accelerates convergence rate and eases the difficulty for reconstructing high-resolution (HR) image, these CNN models can achieve higher peak signal to noise ratio (PSNR) values with lower training cost. However, residual image used in present residual network still contains much high frequency information, which increases learning burden and limits learning ability of residual network. Moreover, training a very deep network faces many obstacles and costs too much time. In this paper, we propose a multiple residual learning network (MRLN), which not only further simplifies information complexity of residual image and improves the accuracy of residual network, but also obviously reduces time cost for training a very deep CNN. In MRLN, we use a shallow network formed by 30-layer convolutional layers as basic model and train it for multiple times. The output of previous basic model is used as the HR input of the next one. In this way, an extremely large CNN is converted into a series connection of shallow networks. Fig. 1 shows PSNR of recent state-of-the-art CNN models for scale factor 2 on Set5, our method performs better than other methods and set a new level for SR.

关键词： super-resolution multiple residual learning convolutional neural network shallow series connection

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Automatic tissue segmentation by deep learning: from colorectal polyps in colonoscopy to abdominal organs in CT exam 33

Automatic tissue segmentation by deep learning: from colorec...

引用

33rd IEEE International conference on visual communications and image processing (IEEE VCIP)

作者： Huang, Cheng-Hsien Xiao, Wei-Ting Chang, Li-Jen Tsai, Wei-Ta Liu, Wei-Min Natl Chung Cheng Univ Dept CSIE Chiayi Taiwan Chia Yi Christian Hosp Ditmanson Med Fdn Chiayi Taiwan Buddhist Dalin Tzu Chi Hosp Dept Oncol Chiayi Taiwan

ISBN: (纸本)9781538644584

Automatic tissue segmentation is extremely helpful in medical imaging related work. In this paper, we attempted to train two existing deep neural networks, SegNet and DeepLab, to solve two clinical imaging problems relevant to this topic. One is to locate the colorectal polyps in the colonoscopy images, and the other is to delineate the lung in CT images from axial direction. In order to enhance the segmentation capability of the two networks, the reversed version of long short term memory (LSTM) network are integrated with them by parallel connection. The performance is evaluated by mean intersection-over-union (IOU). We found that introducing LSTM is beneficial to segmentation of polyps, but not that significant for delineating the lung. The relevant results are reported in this work

关键词： Colon cancer CT image organ segmentation Radiotherapy Deep learning Long short term memory network

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Compressed Sensing via a Deep Convolutional Auto-encoder 33

Compressed Sensing via a Deep Convolutional Auto-encoder

引用

33rd IEEE International conference on visual communications and image processing (IEEE VCIP)

作者： Wu, Hao Zheng, Ziyang Li, Yong Dai, Wenrui Xiong, Hongkai Shanghai Jiao Tong Univ Dept Elect Engn Shanghai Peoples R China Univ Texas Hlth Sci Ctr Houston Sch Biomed Informat Houston TX 77030 USA

ISBN: (纸本)9781538644584

The nonlinear recovery is not promising in accuracy and speed, which limits the practical usage of compressed sensing (CS). This paper proposes a deep learning-based CS framework which leverages a deep convolutional auto-encoder for image sensing and recovery. The utilized auto-encoder architecture consists of three components: the fully convolutional network acts as an adaptive measurement matrix generator in the encoder;while in the decoder, the deconvolution network and refined reconstruction network are learned for intermediate and final recovery, respectively. Different from most previous work focusing on the block-wise manner to reduce implementation cost but result in blocky artifacts, our adaptive measurement matrix is applicable to any size of scene image and the decoder network reconstructs the whole image efficiently without any blocky artifacts. Moreover, dense connectivity is leveraged to combine multi-level features and alleviate the vanishing-gradient problem in the refined reconstruction network which boosts the performance on image recovery. Compared to the state-of-the-art methods, our algorithm improves more than 0.8 dB in average PSNR.

关键词： Compressed sensing Adaptive measurement matrix Convolutional auto-encoder image reconstruction

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Solder Void Detection of Bottom Terminated Components on Printed Circuit Board for Low-Resolution X-Ray images 26

Solder Void Detection of Bottom Terminated Components on Pri...

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26th IEEE Signal processing and communications Applications conference (SIU)

作者： Guney, Arda Bayraktar, Murat Ozcan, Muhammet VLE Elekt Otomotiv San & Tic AS TOSB Otomotiv Yan Sanayi Ihtisas Org San Bol 2 Cadde 12 TR-41420 Cayirova Kocaeli Turkey

ISBN: (纸本)9781538615010

Bottom terminated circuit components with their low cost, improved signal speeds and good thermal performances have made the use of these elements increasingly widespread in recent years. However, in the post assembly inspection of these components solder voids can be encountered at a level of effecting the functionality and reliability of the board. It is difficult to detect solder voids manually by visual inspection;therefore x-ray systems have to be used for the detection of solder voids. Solder voids can not be accurately detected with the software on some existing low resolution x-ray control system, a manual adjustment was required on the received images. In this study;images received from low resolution x-ray device were processed with the help of various image processing algorithms and a graphical user interface program that allows automotic detection of different tyoes of solder voids was created. The work has been proven by using real x-ray images, which yield more accurate results than the existing x-ray software.

关键词： solder void x-ray SMD component printed circuit board bottom terminated component image processing

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Dense Residual Convolutional Neural Network based In-Loop Filter for HEVC 33

Dense Residual Convolutional Neural Network based In-Loop Fi...

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33rd IEEE International conference on visual communications and image processing (IEEE VCIP)

作者： Wang, Yingbin Zhu, Han Li, Yiming Chen, Zhenzhong Liu, Shan Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan Hubei Peoples R China Wuhan Univ Sch Comp Sci Wuhan Hubei Peoples R China Tencent Media Lab Palo Alto CA USA

ISBN: (纸本)9781538644584

In-loop filtering is a key technique in the state of art video coding standard that plays a significant role in suppressing compression artifacts. Motivated by the latest advances of deep learning, in this paper, we design a dense residual convolutional neural network (DRN) based in-loop filter for High Efficiency Video Coding (HEVC). Taking advantage of both dense shortcuts and residual learning, DRN efficiently exploits the multi-level features to restore the high quality image from the degraded one. Bottleneck layers are employed in DRN in order to adaptively fuse the hierarchical features and saving the computational resources at the same time. Experimental results show that the proposed DRN based in-loop filter can further boost the coding performance, which provides 6.9% BD-rate reduction on average compared to the HEVC baseline. In addition, the proposed DRN outperforms previous CNN based in-loop filters.

关键词： In-Loop Filtering High Efficiency Video Coding (HEVC) Video Compression Convolutional Neural Network (CNN) image Restoration

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An Improved and Optimized Content-Aware Resizing Algorithm for images with Densely Situated Foreground Objects 3

An Improved and Optimized Content-Aware Resizing Algorithm f...

引用

IEEE International conference on Wireless communications, Signal processing and Networking (IEEE WiSPNET)

作者： Roy, Soumyakanti Dasgupta, Tanmoy Pradhan, Tapan Techno India Univ Dept Elect Engn Kolkata W Bengal India

ISBN: (纸本)9781538636244

An image in general consists of a combination of significant objects in the foreground and not-so-significant objects in the background. Content aware image resizing or seam carving is a process of resizing an image while maintaining the significant objects (the foreground) in proper visual saliency. The standard algorithms, however, often generate unpredictable distortions in images with densely situated foreground objects. The optimized content aware image resizing (OCAIR) algorithm presented herein, uses iterative graph cuts and edge detection to generate an energy map based on the important sections of the image, so that the resized image does not exhibit unpredictable artefacts. An improved energy map generation algorithm is designed here, which not only marks out the important foreground elements quicker than previously available techniques, but also uses that information to quantify the amount of distortion (if any) that might take place after adding or deleting seams by means of calculating a distortion factor. The process being considerably faster than previous algorithms, allows precise modifications to the input parameters to obtain a well-doctored final image.

关键词： Content-aware image resizing seam carving GrabCut

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Combining Intra Block Copy and Neighboring Samples Using Convolutional Neural Network for image Coding 33

Combining Intra Block Copy and Neighboring Samples Using Con...

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33rd IEEE International conference on visual communications and image processing (IEEE VCIP)

作者： Zhang, Zhaobin Li, Yue Li, Li Li, Zhu Liu, Shan Univ Missouri Dept Comp Sci Elect Engn Kansas City MO 64110 USA Univ Sci & Technol China Dept Elect Engn Informat Sci Hefei Anhui Peoples R China Tencent Amer Media Lab Palo Alto CA USA Univ Missouri Kansas City MO 64110 USA

ISBN: (纸本)9781538644584

Intra prediction is an essential step to remove spatial redundancy in image coding. How to best predict current block given surrounding pixels is the key efficiency challenge. Inspired by the recent success in applying deep learning to image/video coding systems, we propose an intra prediction method by combining intra block copy and neighboring samples using convolutional neural networks. A novel CNN is developed to further exploit the spatial correlation. Instead of only considering local information, the proposed method can infer the current block via fusing the non-local recurrent features, which is captured by intra block copy, with the local samples located at the left and above boundaries of current block. We also investigate how the performance is affected by the way of fusing IBC and reference boundary pixels. In additional, training data pre-processing is studied to enable the CNN with a better learning capability. Simulation results yield promising coding gain and indicate great potential ability that CNN can be used for next generation video coding framework.

关键词： Convolutional neural network (CNN) Intra prediction High Efficiency Video Coding (HEVC) image coding Intra block copy

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Efficient 3D Liver Segmentation using Enhanced image Alignment

Efficient 3D Liver Segmentation using Enhanced Image Alignme...

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conference on Information communications Technology and Society (ICTAS)

作者： Hiraman, Anura Viriri, Serestina Gwetu, Mandlenkosi Univ KwaZulu Natal Sch Math Stat & Comp Sci Durban South Africa

ISBN: (纸本)9781538610015

An efficient and fully automated algorithm for the alignment of serially acquired 2D slices forming a 3D volume is presented. The methodology includes four stages: pre-processing using a median filter, obtaining a threshold value via a Laplacian weighted histogram, skeletonization and image alignment. The introduction of the skeletonization in this approach ensures that the images are being aligned with respect to the bones and large organs. The image alignment algorithm relies on the Enhanced Correlation Coefficient (ECC) alignment algorithm. The presented approach is evaluated on the Medical image Computing and Computer Assisted Intervention (MICCAI) 2007 grand challenge datasets and the evaluation metric used is mean squared difference (MSD). The proposed approach proved to perform well and achieved an improvement in the MSD by an average of 32.85%.

关键词： Liver segmentation Skeletonization image alignment MICCAI 2007 Liver dataset

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Comprehensive Samples Constrain for Person Search 33

Comprehensive Samples Constrain for Person Search

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33rd IEEE International conference on visual communications and image processing (IEEE VCIP)

作者： Li, Liangqi Yang, Hua Chen, Lin Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai Peoples R China Natl Engn Lab Publ Secur Risk Percept & Control B Beijing Peoples R China

ISBN: (纸本)9781538644584

In this paper, we propose a method to further improve person search by fully utilizing the combination of pedestrian detection and person re-identification tasks. An improved constrain that utilizes comprehensive samples in the dataset is proposed to fully excavate information for recognition. Besides the label constrain for training the model in traditional classification task, unlabeled identities that do not have specific IDs are utilized as well to constitute a tailored triplet loss for more performance improvement. Meanwhile, a novel large-scale challenging dataset, SJTU318, which uses videos acquired through twelve cameras is proposed to demonstrate the effectiveness of our method. It contains 443 identities and 14,610 frames in which pedestrians are annotated with their bounding box positions and identities. Experiments conducted on a public dataset, CUHK-SYSU and our proposed dataset SJTU318 show that our method outperforms existing state-of-the-art approaches.

关键词： Pedestrian detection person re-identification person search deep learning triplet loss

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