2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP)最新文献_第4页

Enhanced Steerable Pyramid Transformation for Medical Ultrasound Image Despeckling 增强的可操纵金字塔变换用于医学超声图像去斑

2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2018-08-01 DOI: 10.1109/MMSP.2018.8547091

Prerna Singh, R. Mukundan, Rex de Ryke

{"title":"Enhanced Steerable Pyramid Transformation for Medical Ultrasound Image Despeckling","authors":"Prerna Singh, R. Mukundan, Rex de Ryke","doi":"10.1109/MMSP.2018.8547091","DOIUrl":"https://doi.org/10.1109/MMSP.2018.8547091","url":null,"abstract":"The paper presents a novel approach for suppressing speckle noise at the same time preserving edge information effectively in ultrasound images for better clinical analysis and problem identification. The framework includes the modified adaptive Wiener filter (MAWF) along with the Canny edge detection method and enhanced steerable pyramid transformation (SPT) algorithm. The Canny algorithm is used to detect the true edges from the noisy ultrasound (US) image, and the MAWF algorithm smoothens the speckle effect without affecting the edge information which is preserved separately and added to the final output. The discrete Fourier transform (DFT) is used to extract the low and high frequency coefficients. Unlike other multiresolution techniques used for speckle suppression, the proposed method uses the steerable pyramid transformation technique based on high frequency components extracted using DFT for image enhancement. The coherence component extraction (CCE) method enhances the overall texture and edge features of the image even in the darker portions of the image. The output of this stage is finally combined with the stored edge information. This paper also presents experimental results to show that the proposed technique outperforms other state-of-art techniques in terms of peak signal to noise ratio, structural similarity index, and universal quality index.","PeriodicalId":137522,"journal":{"name":"2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124372209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Rethinking Recurrent Latent Variable Model for Music Composition 再思考音乐作曲的循环潜变量模型

2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2018-08-01 DOI: 10.1109/MMSP.2018.8547061

E. Koh, S. Dubnov, Dustin Wright

引用次数: 16

Feature Fusion for Robust Patch Matching with Compact Binary Descriptors 基于压缩二进制描述符的鲁棒补丁匹配特征融合

2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2018-08-01 DOI: 10.1109/MMSP.2018.8547141

Andrea Migliorati, A. Fiandrotti, Gianluca Francini, S. Lepsøy, R. Leonardi

引用次数: 1

Decoding Music in the Human Brain Using EEG Data 利用脑电图数据解码人脑中的音乐

2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2018-08-01 DOI: 10.1109/MMSP.2018.8547051

Chris Foster, Dhanush Dharmaretnam, Haoyan Xu, Alona Fyshe, G. Tzanetakis

引用次数: 4

A Deep Convolutional Network Based Supervised Coarse-to-Fine Algorithm for Optical Flow Measurement 基于深度卷积网络的监督粗到精光流测量算法

2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2018-08-01 DOI: 10.1109/MMSP.2018.8547130

Meiyuan Fang, Yanghao Li, Yuxing Han, Jiangtao Wen

引用次数: 1

A Robust HER2 Neural Network Classification Algorithm Using Biomarker-Specific Feature Descriptors 基于生物标志物特异性特征描述符的鲁棒HER2神经网络分类算法

2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2018-08-01 DOI: 10.1109/MMSP.2018.8547043

Prerna Singh, R. Mukundan

引用次数: 8

Image Inpainting Detection Based on a Modified Formulation of Canonical Correlation Analysis 基于典型相关分析改进公式的图像修补检测

2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2018-08-01 DOI: 10.1109/MMSP.2018.8547106

Xiao Jin, Yuting Su, Yongwei Wang, Z. J. Wang

{"title":"Image Inpainting Detection Based on a Modified Formulation of Canonical Correlation Analysis","authors":"Xiao Jin, Yuting Su, Yongwei Wang, Z. J. Wang","doi":"10.1109/MMSP.2018.8547106","DOIUrl":"https://doi.org/10.1109/MMSP.2018.8547106","url":null,"abstract":"Image inpainting is a common image editing technique for filling the missing areas in images. It can be adopted to destroy the integrity of images by forgers with ulterior motives. Compared with other types of inpainting, sparsity-based inpainting assumes more general prior knowledge and is more widely used in practical applications. Although several methods for detecting exemplar-based and diffusion-based inpainting have been proposed, there is a shortage of effective scheme for detecting sparsity-based inpainting. In this paper, we proposed a novel algorithm for sparsity-based image inpainting detection. This type of inpainting has a strong effect on the coefficients of Canonical Correlation Analysis (CCA). Based on this observation, a modified objective function of CCA and a corresponding optimization algorithm are further developed to enhance the difference of inter-class in our feature set. The experiments implemented on two publicly available datasets demonstrated our method's superiority over other competitors. Particularly, unlike previous inpainting detection methods, the proposed framework has better performance in the case of JPEG compression.","PeriodicalId":137522,"journal":{"name":"2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP)","volume":"167 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123874876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Fast, Robust, and Accurate Image Denoising via Very Deeply Cascaded Residual Networks 快速，鲁棒和准确的图像去噪通过非常深级联残差网络

2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2018-08-01 DOI: 10.1109/MMSP.2018.8547119

Lulu Sun, Yongbing Zhang, Xingzheng Wang, Haoqian Wang, Qionghai Dai

{"title":"Fast, Robust, and Accurate Image Denoising via Very Deeply Cascaded Residual Networks","authors":"Lulu Sun, Yongbing Zhang, Xingzheng Wang, Haoqian Wang, Qionghai Dai","doi":"10.1109/MMSP.2018.8547119","DOIUrl":"https://doi.org/10.1109/MMSP.2018.8547119","url":null,"abstract":"Patch based image modelings have shown great potential in image denoising. They mainly exploit the nonlocal self-similarity (NSS) of either input degraded images or clean natural ones when training models, while failing to learn the mappings between them. More seriously, these algorithms have very high time complexity and poor robustness when handling images with different noise variances and resolutions. To address these problems, in this paper, we propose very deeply cascaded residual networks (VDCRN) to build the precise relationships between the noisy images and their corresponding noise-free ones. It adopts a new residual unit with an identity skip connection (shortcut) to make training easy and improve generalization. The introduction of shortcut is helpful to avoid the problem of gradient vanishing and preserve more image details. By cascading three such residual units, we build the VDCRN to deploy deeper and larger convolutional networks. Based on such a residual network, our VDCRN achieves very fast speed and good robustness. Experimental results demonstrate that our model outperforms a lot of state-of-the-art denoising algorithms quantitively and qualitively.","PeriodicalId":137522,"journal":{"name":"2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP)","volume":"516 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116540603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

CPNet: A Context Preserver Convolutional Neural Network for Detecting Shadows in Single RGB Images CPNet:一种用于检测单个RGB图像阴影的上下文保护卷积神经网络

2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2018-08-01 DOI: 10.1109/MMSP.2018.8547080

S. Mohajerani, Parvaneh Saeedi

引用次数: 15

A Sub-Aperture Image Selection Refinement Method for Progressive Light Field Transmission 渐进式光场传输的子孔径选像细化方法

2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP) Pub Date : 2018-08-01 DOI: 10.1109/MMSP.2018.8547069

Wallace Bruno S. de Souza, B. Macchiavello, Eduardo Peixoto, E. Hung, Gene Cheung

{"title":"A Sub-Aperture Image Selection Refinement Method for Progressive Light Field Transmission","authors":"Wallace Bruno S. de Souza, B. Macchiavello, Eduardo Peixoto, E. Hung, Gene Cheung","doi":"10.1109/MMSP.2018.8547069","DOIUrl":"https://doi.org/10.1109/MMSP.2018.8547069","url":null,"abstract":"Light field cameras capture the emanated light from a scene. This type of images allows for changing point of views or focal points by processing the captured information. Recently, a Progressive Light Field Communication (PLFC) was proposed. PLFC addresses an interactive Light Field (LF) streaming framework, where a client requests a certain view or focal point and a server synthesizes and transmits each requested image as a linear combination of Sub-Aperture Images (SAI). The main idea of PLFC is that as the virtual views are transmitted, the client gradually learns information about the LF, so eventually the client may posses enough information to locally create the virtual view at the required quality, avoiding the transmission of a new image. In order to PLFC work, an optimization algorithm which selects the SAIs that are used to create a certain virtual view is requested. Here, we improve over the previous PLFC proposal by presenting a method that focuses on a refinement algorithm for SAI selection, using dynamic Quantization Parameter (QP) during encoding, using an automatic method to determine the Lagrangian multiplier during optimization and modifying how the initial required cache is created. These proposed changes in the algorithm produce significant gains. The results shows gains up to 85.8% on BD-rate compared to trivial LF transmissions, whereas they're up to 32.8% compared to previous PLFC.","PeriodicalId":137522,"journal":{"name":"2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP)","volume":"166 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123178525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1