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Improved PSP-Net Segmentation Network for Automatic Detection of Neovascularization in Color Fundus images

Improved PSP-Net Segmentation Network for Automatic Detectio...

引用

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

作者： Liu, Qiuming Wang, Shen Dai, Yulan Zhang, Jintao Wang, Yichen Zhou, Ruoxuan Jiangxi Univ Sci & Tech Sch Software Engn Ganzhou Jiangxi Peoples R China Cent South Univ Sch Comp Sci & Engn Changsha Peoples R China Jiangxi Normal Univ Sch Comp Info Engn Nanchang Jiangxi Peoples R China Nanchang Key Lab Virtual Digital Factory & Cultur Nanchang Jiangxi Peoples R China

ISBN: (纸本)9781665475921

Proliferative Diabetic Retinopathy (PDR) is a serious retinal disease threatening diabetic patients. Intense retinal neovascularization in the retinal image is the most important clinical symptom of PDR, leading to visual distortion if not controlled. Accurate and timely detection of neovascularization from retinal images allows patients to receive adequate treatment to avoid further vision loss. In this work, we propose a retinal neovascularization automatic segmentation model based on improved Pyramid Scene Parsing Network (PSP-Net). To improve the accuracy of the model, we introduce the proposed channel attention module into the model. The network is evaluated with color fundus images from practice. Evaluation results show the network is superior to FCN, SegNet, U-Net and PSP-Net in accuracy and sensitivity. The model could achieve accuracy, sensitivity, specificity, precision and Jaccard similarity score of 0.9832, 0.9265, 0.9897, 0.9116 and 0.8501, respectively. This paper proves through plenty of experimental results that the network model is able to improve the accuracy of segmentation, relieve the workload of doctors, and is worthy of further clinical promotion.

关键词： fundus images image segmentation diabetic retinopath neovascularization deep learning

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Block turbo codes for efficient image transmission over wireless channels

Block turbo codes for efficient image transmission over wire...

引用

conference on visual communications and image processing 2004

作者： Zaïbi, S Ammar, M Amis, K Pyndiah, R Saoudi, S TAMCIC CNRS 2658 Dept Signal & Commun PRAComGET ENST Bretagne F-29238 Brest France

ISBN: (纸本)0819452114

In this paper we propose an image coding scheme based on Block Turbo Codes (BTC) for protecting SPIHT coded images transmitted over wireless channels. An Unequal Error Protection (UEP) scheme using different product codes is also presented. The optimization of the UEP scheme under a transmission budget constraint is investigated. Performance of the proposed system are first evaluated over Gilbert-Elliott (GE) channels. They are then compared to the well-known system of Sherwood and Zeger,(1) through numerical simulations over a single path Rayleigh fading channel. Then, we present an optimized mobile image transmission framework involving a multiuser asynchronous DS-CDMA transmission system over Rayleigh multipath channels. Simulation results for the 512 x 512 Lena image. transmitted over the ITU Vehicular A channel. show that a significant gain in PSNR. is obtained by using theSIC/RAKE multiuser dectector(2) in comparison with a conventional detection by the RAKE receiver. Furthermore, performance are also improved, when introducing the UEP scheme.

关键词： SPIHT block turbo codes unequal error protection rate allocation DS-CDMA multiuser detection

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EFFICIENT ACTIVE CONTOUR MODEL BASED ON VESE-CHAN MODEL AND SPLIT BREGMAN METHOD

EFFICIENT ACTIVE CONTOUR MODEL BASED ON VESE-CHAN MODEL AND ...

引用

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

作者： Yang, Yunyun Zhao, Yi Harbin Inst Technol Shenzhen Grad Sch Shenzhen 518055 Peoples R China

ISBN: (纸本)9781479902880

In this paper we propose an efficient multi-phase image segmentation for color images based on the piecewise constant multi-phase Vese-Chan model and the split Bregman method. The proposed model is first presented in a four-phase level set formulation and then extended to a multi-phase formulation. The four-phase and multi-phase energy functionals are defined and the corresponding minimization problems of the proposed active contour model are presented. The split Bregman method is applied to minimize the multi-phase energy functional efficiently. The proposed model has been applied to synthetic and real color images with promising results. The advantages of the proposed active contour model have been demonstrated by numerical results.

关键词： Active contour model multi-phase image segmentation split Bregman method level set method

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Multi-GRU Based Automated image Captioning for Smartphones 29

Multi-GRU Based Automated Image Captioning for Smartphones

引用

29th IEEE conference on Signal processing and communications Applications (SIU)

作者： Keskin, Rumeysa Moral, Ozge Taylan Kilic, Volkan Onan, Aytug Izmir Katip Celebi Univ Dept Elect & Elect Engn Izmir Turkey Izmir Katip Celebi Univ Dept Comp Engn Izmir Turkey

ISBN: (纸本)9781665436496

image captioning is the description of an image with natural language expressions using computer vision and natural language processing fields. Recent advances in hardware and processing power in smartphones lead the development of many image captioning applications. In this study, a novel automatic image captioning system based on the encoder-decoder approach that can be applied in smartphones is proposed. While high-level visual information is extracted with the ResNet152V2 convolutional neural network in the encoder part, the proposed decoder transforms the extracted visual information into natural expressions of the images. The proposed decoder with the multilayer gated recurrent unit structure allows generating more meaningful captions using the most relevant visual information. The proposed system has been evaluated using different performance metrics on the MSCOCO dataset and it outperforms the state-of-the-art approaches. The proposed system is also integrated with our custom-designed Android application, named IMECA, which generates captions in offline mode unlike similar applications. Thus, image captioning is intended to be practical for more people.

关键词： image captioning computer vision natural language processing Android application

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Enhancement of Color image Based on Tone-Preserving 8

Enhancement of Color Image Based on Tone-Preserving

引用

8th International conference on Wireless communications and Signal processing (WCSP)

作者： Yang, Ya'nan Wang, Xiaofan Liu, Feng Gan, Zongliang Nanjing Univ Posts & Telecommun Jiangsu Prov Key Lab Image Proc & Image Commun Nanjing 210003 Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Minist Educ Key Lab Broadband Wireless Commun & Sensor Networ Nanjing 210003 Jiangsu Peoples R China

ISBN: (纸本)9781509028603

Since the lighting conditions in strong contrast regions between the light and dark cant be estimated accurately by traditional center/surround Retinex algorithm, the over-enhancement and color distortion may exist. In view of this, combining with the human visual characteristics, a color image enhancement algorithm based on tone-preserving was proposed. A determination function was added to the bilateral filter to estimate illuminance image more accurately and weaken over-enhancement. According to human visual masking effect, the improved gamma correction was utilized to correct the brightness of illumination image adaptively and the local contrast of reflection image obtained by division was enhanced based on local statistics. Besides, the final enhanced image was obtained by combining illumination image with reflection image, which can make image appear more natural. Compared with other similar algorithms from both subjective and objective aspects, the results show that this method being applied to low-contrast color image enhancement can not only improve image clarity, but reduce color distortion.

关键词： Retinex tone-preserving bilateral filter masking effect local contrast

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Fully Neural Network Mode Based Intra Prediction of Variable Block Size

Fully Neural Network Mode Based Intra Prediction of Variable...

引用

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

作者： Sun, Heming Yu, Lu Katto, Jiro Waseda Univ Waseda Res Inst Sci & Engn Tokyo Japan JST PRESTO 4-1-8 Honcho Kawaguchi Saitama Japan Zhejiang Univ Inst Informat & Commun Engn Hangzhou Peoples R China Waseda Univ Dept Comp Sci & Commun Engn Tokyo Japan

ISBN: (纸本)9781728180687

Intra prediction is an essential component in the image coding. This paper gives an intra prediction framework completely based on neural network modes (NM). Each NM can be regarded as a regression from the neighboring reference blocks to the current coding block. (1) For variable block size, we utilize different network structures. For small blocks 4x4 and 8x8, fully connected networks are used, while for large blocks 16x16 and 32x32, convolutional neural networks are exploited. (2) For each prediction mode, we develop a specific pre-trained network to boost the regression accuracy. When integrating into HEVC test model, we can save 3.55%, 3.03% and 3.27% BD-rate for Y, U, V components compared with the anchor. As far as we know, this is the first work to explore a fully NM based framework for intra prediction, and we reach a better coding gain with a lower complexity compared with the previous work.

关键词： Intra prediction image compression deep learning fully connected layer convolutional neural network

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Content-Adaptive Rate-Quality Curve Prediction Model in Media processing System

Content-Adaptive Rate-Quality Curve Prediction Model in Medi...

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2024 conference on visual communications and image processing

作者： Yin, Shibo Zhang, Zhiyu Ning, Peirong Chen, Qiubo Chen, Jing Zhou, Quan Lu, Guo Song, Li Xiaohongshu Inc Shanghai Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China

ISBN: (纸本)9798331529543;9798331529550

In streaming media services, video transcoding is a common practice to alleviate bandwidth demands. Unfortunately, traditional methods employing a uniform rate factor (RF) across all videos often result in significant inefficiencies. Content-adaptive encoding (CAE) techniques address this by dynamically adjusting encoding parameters based on video content characteristics. However, existing CAE methods are often tightly coupled with specific encoding strategies, leading to inflexibility. In this paper, we propose a model that predicts both RF-quality and RF-bitrate curves, which can be utilized to derive a comprehensive bitrate-quality curve. This approach facilitates flexible adjustments to the encoding strategy without necessitating model retraining. The model leverages codec features, content features, and anchor features to predict the bitrate-quality curve accurately. Additionally, we introduce an anchor suspension method to enhance prediction accuracy. Experiments confirm that the actual quality metric (VMAF) of the compressed video stays within +/- 1 of the target, achieving an accuracy of 99.14%. By incorporating our quality improvement strategy with the rate-quality curve prediction model, we conducted online A/B tests, obtaining both +0.107% improvements in video views and video completions and +0.064% app duration time. Our model has been deployed on the Xiaohongshu App.

关键词： Prediction models

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A Practical Approach to Depth-Aware Augmentation for Neural Radiance Fields

A Practical Approach to Depth-Aware Augmentation for Neural ...

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2024 conference on visual communications and image processing

作者： Khosroshahi, Hamed Razavi Sancho, Jaime Bonatto, Daniele Fachada, Sarah Bang, Gun Lafruit, Gauthier Juarez, Eduardo Teratani, Mehrdad Univ Libre Bruxelles Lab Image Synth & Anal Brussels Belgium Univ Politecn Madrid Res Ctr Software Technol & Multimedia Syst Madrid Spain Telecommun Res Inst Elect Daejeon South Korea

ISBN: (纸本)9798331529543;9798331529550

Neural Radiance Fields (NeRF) have demonstrated exceptional performance in generating novel views of scenes by learning implicit volumetric representations from calibrated RGB images, without depth information. A major limitation is the need for large training datasets in neural network-based view synthesis frameworks. The challenge of effective data augmentation for view synthesis remains unresolved. NeRF models require extensive scene coverage from multiple views to accurately estimate radiance and density. Insufficient coverage reduces the model's ability to interpolate or extrapolate unseen parts of the scene effectively. In this paper, we propose a novel pipeline to address this data augmentation issue using depth map information. We use depth image-based rendering (DIBR) to overcome the lack of enough views for training NeRF. Experimental results indicate that our approach enhances the quality of rendered images using the NeRF framework, achieving an average peak signal-to-noise ratio (PSNR) increase of 7.2 dB, with a maximum improvement of 12 dB.

关键词： Neural Radiance Fields NeRF View synthesis Data augmentation Depth map

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CNN-Based Post-processing Filter for Video Compression with Multi-Scale Feature Representation

CNN-Based Post-Processing Filter for Video Compression with ...

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

作者： Qi, Zhanyuan Jung, Cheolkon Liu, Yang Li, Ming Xidian Univ Sch Elect Engn Xian Peoples R China OPPO Xian Peoples R China

ISBN: (纸本)9781665475921

In this paper, we propose a convolutional neural network (CNN)-based post-processing filter for video compression with multi-scale feature representation. The discrete wavelet transform (DWT) decomposes an image into multi-frequency and multi-directional sub-bands, and can figure out artifacts caused by video compression with multi-scale feature representation. Thus, we combine DWT with CNN and construct two sub-networks: Step-like sub-band network (SLSB) and mixed enhancement network (ME). SLSB takes the wavelet subbands as input, and feeds them into the Res2Net group (R2NG) from high frequency to low frequency. R2NG consists of Res2Net modules and adopts spatial and channel attentions to adaptively enhance features. We combine the high frequency sub-band output with the low frequency sub-band in R2NG to capture multi-scale features. ME uses mixed convolution composed of dilated convolution and standard convolution as the basic block to expand the receptive field without blind spots in dilated convolution and further improve the reconstruction quality. Experimental results demonstrate that the proposed CNN filter achieves average 2.13 %, 2.63 %, 2.99 %, 4.8 %, 3.72 % and 4.5 % BD-rate reductions over VTM 11.0-NNVC anchor for Y channel on A1, A2, B, C, D and E classes of the common test conditions (CTC) in AI, RA and LDP configurations, respectively.

关键词： Convolutional neural network attention mechanism compressed video restoration dilated convolution wavelet

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Estimating blockness distortion for performance evaluation of picture coding algorithms

Estimating blockness distortion for performance evaluation o...

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1997 IEEE Pacific Rim conference on communications, Computers and Signal processing

作者： Giusto, DD Perra, M Univ of Cagliari Cagliari Italy

ISBN: (纸本)0780339061

In this paper, some of the most significant image quality indexes are reviewed and compared with a new method for blockness distortion evaluation. The paper begins with a brief survey on classical measures based on numerical difference between original and reconstructed image data (e.g., MSE, SNR and PSNR) and advanced methods aiming at considering the perceptive aspects of image degradation (e.g., Hosaka Plots and other methods based on Human visual System properties like Information Content or Perceptual image Distortion). After, four innovative methods for blockness distortion measurement are proposed: two based on DCT analysis, and two on differential Sobel operator. Results on standard pictures confirm the efficiency of the proposed measures.

关键词： image coding

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