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IEEE visual communications and image processing (VCIP)

作者： Qi Chen Fang Liu Huiyu Duan Yao Wang Xiongkuo Min Yan Zhou Guangtao Zhai Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai China Department of Radiology Renji Hospital School of Medicine Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781665475938

Magnetic Resonance Imaging (MRI) is widely used for medical diagnosis, staging and follow-up of disease. However, MRI images may have artifacts due to various reasons such as patient movement or machine distortion, which may be unintentionally introduced during the procedure of medical image acquisition, processing, etc. These artifacts may affect the effectiveness of diagnosis or even cause false diagnosis. To solve this problem, we propose a general medical image quality assessment (MIQA) methodology, including subjective MIQA procedures and objective MIQA algorithms. We further apply this methodology to MRI images in this paper due to its widespread use in practical applications. We first establish a magnetic resonance imaging quality assessment (MRIQA) database, which contains 3809 MRI images. Then a subjective image quality assessment experiment is conducted by expert doctors according to the diagnostic value of these images, which split all MRI images into 1285 low quality images and 2524 high quality images. We then conduct a baseline deep learning experiment, and propose an attention based MIQANet model to automatically separate MRI images into high quality and low quality based on their diagnosis value. Our proposed method achieves a great quality assessment accuracy of 96.59%. The constructed MRIQA database and proposed MIQA model will be public available to further promote medical IQA research.

关键词： image quality Deep learning Databases visual communication Magnetic resonance imaging Neural networks Magnetic resonance

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A Weighted Mean Absolute Error Metric for image Quality Assessment

A Weighted Mean Absolute Error Metric for Image Quality Asse...

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

作者： Hao, Sihan Li, Sumei Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China

ISBN: (纸本)9781728180687

Pixel-wise image quality assessment (IQA) algorithms, such as mean square error (MSE), mean absolute error (MAE) and peak signal-to-noise ratio (PSNR) correlate well with perceptual quality when dealing with images sharing the same distortion type but not well when processing images in different distortion types, which is inconsistent with human visual system (HVS). Although a large number of metrics based on image error has been proposed, there are still difficulties and limitations. To solve this problem, a full reference image quality assessment (FR-IQA) method based on MAE is proposed in this paper. The metric divides the image error (difference between distorted image and reference image) map into smooth region and texture-edge region, calculates their mean values respectively, and then gives them different weights considering the masking effect. The key innovation of this paper is to propose a distortion significance measurement, which is a visual quality coefficient that can effectively indicate the influence of different distortion types on perceptual quality and unify them with HVS. The segmented image error maps are weighted by the distortion significance coefficient. The experimental results on four largest benchmark databases show that the most of the distortions are successfully evaluated and the results are consistent with HVS.

关键词： Error map MAE FR-IQA distortion significance coefficient

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No-Reference Stereoscopic image Quality Assessment Based On visual Attention Mechanism

No-Reference Stereoscopic Image Quality Assessment Based On ...

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

作者： Li, Sumei Zhao, Ping Chang, Yongli Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China

ISBN: (纸本)9781728180687

In this paper, we proposed an optimized model based on the visual attention mechanism(VAM) for no-reference stereoscopic image quality assessment (SIQA). A CNN model is designed based on dual attention mechanism (DAM), which includes channel attention mechanism and spatial attention mechanism. The channel attention mechanism can give high weight to the features with large contribution to final quality, and small weight to features with low contribution. The spatial attention mechanism considers the inner region of a feature, and different areas are assigned different weights according to the importance of the region within the feature. In addition, data selection strategy is designed for CNN model. According to VAM, visual saliency is applied to guide data selection, and a certain proportion of saliency patches are employed to fine tune the network. The same operation is performed on the test set, which can remove data redundancy and improve algorithm performance. Experimental results on two public databases show that the proposed model is superior to the state-of-the-art SIQA methods. Cross-database validation shows high generalization ability and high effectiveness of our model.

关键词： stereoscopic image visual attention mechanism dual attention mechanism data selection

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Cleaning Highly Unbalanced Multisource image Dataset for Quality Control in Cervical Precancer Screening 4th

Cleaning Highly Unbalanced Multisource Image Dataset for Qua...

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4th International conference on Recent Trends in image processing and Pattern Recognition, RTIP2R 2021

作者： Xue, Zhiyun Guo, Peng Angara, Sandeep Pal, Anabik Jeronimo, Jose Desai, Kanan T. Ajenifuja, Olusegun K. Adepiti, Clement A. Sanjose, Silvia D. Schiffman, Mark Antani, Sameer National Library of Medicine BethesdaMD20894 United States National Cancer Institute RockvilleMD20850 United States Obafemi Awolowo University Ile-Ife Nigeria

ISBN: (纸本)9783031070044

Automated visual evaluation (AVE) of uterine cervix images is a deep learning algorithm that aims to improve cervical pre-cancer screening in low or medium resource regions (LMRR). image quality control is an important pre-step in the development and use of AVE. In our work, we use data retrospectively collected from different sources/providers for analysis. In addition to good images, the datasets include low-quality images, green-filter images, and post Lugol’s iodine images. The latter two are uncommon in VIA (visual inspection with acetic acid) and should be removed along with low-quality images. In this paper, we apply and compare two state-of-the-art deep learning networks to filter out those two types of cervix images after cervix detection. One of the deep learning networks is DeepSAD, a semi-supervised anomaly detection network, while the other is ResNeSt, an improved variant of the ResNet classification network. Specifically, we study and evaluate the algorithms on a highly unbalanced large dataset consisting of four subsets from different geographic regions acquired with different imaging device types. We also examine the cross-dataset performance of the algorithms. Both networks can achieve high performance (accuracy above 97% and F1 score above 94%) on the test set. © Springer Nature Switzerland AG 2022.

关键词： Iodine

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Low Light RAW image Enhancement Using Paired Fast Fourier Convolution and Transformer

Low Light RAW Image Enhancement Using Paired Fast Fourier Co...

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IEEE visual communications and image processing (VCIP)

作者： Yichi Zhang Hengyu Liu Dandan Ding Zhan Ma School of Information Science and Technology Hangzhou Normal University Hangzhou China School of Electronic Science and Engineering Nanjing University Nanjing China

ISBN: (纸本)9781665475938

Compared to RGB images non-linearly mapped from RAW data through the image Signal Processor (ISP), RAW data are linear to scene radiance and contain more native information, which is better to be modeled in many vision tasks. This work proposes to enhance low-light images in the RAW domain via a cross-scale framework using paired Fast Fourier Convolution (FFC) and Transformer, driving the network to characterize images effectively. The entire framework has three scales to abstract low-level, mid-level, and high-level representations of input images. We embed paired FFC and Transformer in each scale to attain spatial-spectral information extraction and aggregation. Specifically, by transforming features from the spatial domain into the spectral domain with FFC, pixel correlations can be effectively exploited locally and globally, generating representative features for the input image. Immediately, the Transformer using multi-head self-attention mechanism is applied to aggregate and embed important features. Experimental results demonstrate that our method significantly outperforms state-of-the-art low-light enhancement works in both full reference assessment metrics, including PSNR, MPSNR, and SSIM, and no-reference metrics, such as NIMA. Meanwhile, the perceptual quality of the proposed method is more visually pleasing than that of other methods.

关键词： Measurement Convolution visual communication Transforms Transformers Feature extraction Information retrieval

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Eye Movement Analysis using Virtual Reality in Early Detection of Alzheimer’s Disease

Eye Movement Analysis using Virtual Reality in Early Detecti...

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image processing, Computer Vision and Machine Learning (ICICML), International conference on

作者： Haiwei Zuo King Hann Lim Eswaran Sivaraman Jonathan Then Sien Phang Shenzhen City Polytechnic School of Information and Communications Technology Curtin Malaysia Research Institute Curtin University Malaysia

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

Early detection of Alzheimer’s disease (AD) is important at the initial stage to provide prior treatment and control. In traditional diagnostic methods, medical doctors perform manual AD checking by asking patients to repeat several round of text reading and game exercises. The quantification of this manual assessment can be subjective due to the profession experience and it requires a further verification using the brain imaging. In this paper, an automated digital AD assessment method using Virtual Reality (VR) console is proposed to evaluate patient’s condition by tracking their eye movement during text reading. First, the eye movement pattern is captured using VR console during text reading. The recorded eye movement is then converted into frequency domain to study its frequency components during reading process. The simulation of AD symptom is set up by prompting subjects to read their familiar native language and unfamiliar language. As a result, the experiment shows that there are significant differences in eye movement patterns between native and unfamiliar language reading. The group of subjects reading unfamiliar language have random eye movement in less efficient visual processing. This pattern is similar to the patients who suffer in the AD symptom. In addition, the frequency features are extracted to study its correlations with reading comprehension performance in order to detect the early AD condition.

关键词： visualization Correlation Tracking Frequency-domain analysis Virtual reality Manuals Frequency conversion Feature extraction Alzheimer's disease Medical diagnostic imaging

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Spectral Analysis of Aerial Light Field for Optimization Sampling and Rendering of Unmanned Aerial Vehicle

Spectral Analysis of Aerial Light Field for Optimization Sam...

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IEEE visual communications and image processing (VCIP)

作者： Qiuming Liu Yichen Wang Ying Wei Lei Xie Changjian Zhu Ruoxuan Zhou School of Software Engineering Jiangxi Univ. of Sci. and Tech. Jiangxi China School of Comm. and Infor. Engineering Chongqing Univ. of Posts and Telecom. Chongqing China School of Electron. and Inform. Comm. Huazhong Univ. of Sci. and Tech. Wuhan China Nanchang Key lab. of Virtual Digital Factory and Cultural Communications Jiangxi China

ISBN: (纸本)9781665475938

The aerial light field (ALF) can render higher quality images of large-scale 3D scenes. In this paper, we apply the ALF technology to study the image captured and novel view rendering of unmanned aerial vehicle (UAV), which exists some problems, such as large scene and depth of field. First, we design an ALF sampling model using spectral analysis of Fourier theory. Based on the ALF sampling model, the exact expression of ALF spectrum is derived. By the spectral support of ALF, we analyze the influence of pitch angle on light field sampling and its bandwidth. Particularly, the bandwidth of ALF can be applied to determine the minimum sampling rate for UAV. Additionally, we design a reconstruction filter that is related to pitch angle to render novel views of UAV. Finally, our experiments show that our sampling and rendering methods can improve the rendering quality of UAV novel view rendering.

关键词： Analytical models Three-dimensional displays visual communication Bandwidth Rendering (computer graphics) Autonomous aerial vehicles Light fields

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Self-supervised Continual Learning for Object Recognition in image Sequences 28th

Self-supervised Continual Learning for Object Recognition in...

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28th International conference on Neural Information processing

作者： Dai, Ruiqi Lefort, Mathieu Armetta, Frederic Guillermin, Mathieu Difiner, Stefan Univ Lyon INSA Lyon Lyon France Univ Claude Bernard Lyon 1 Univ Lyon Lyon France CNRS UMR5205 LIRIS Villeurbanne France Univ Catholique Lyon Lyon France

ISBN: (纸本)9783030923068;9783030923075

The autonomous learning of different objects in images, with a continual and unsupervised context, relies on detecting unknown objects and recognizing known ones based on the learned visual representation. Novelty detection is challenging because of the internal representation drifts of known objects not been seen for a long time. Most existing approaches either perform offline unsupervised learning on a large dataset, or continual supervised learning. Nevertheless, very few existing approaches propose unsupervised continual learning for object recognition. In this paper, we propose a new neural network-based approach for continually learning representations of objects from image sequences, that is able to autonomously detect novel objects and to recognize previously learned ones during training. It is based on a statistical test, performed on internal representations, adapted to counterbalance the concept drift, without storing any image. Experimental results show that our approach outperforms the state of the art on MNIST and Fashion-MNIST datasets. In particular, our approach avoids to over-segment the distribution of clusters, which artificially increases traditional indicators such as clustering accuracy.

关键词： Object recognition

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Multi-stage Locally and Long-range Correlated Feature Fusion for Learned In-loop Filter in VVC

Multi-stage Locally and Long-range Correlated Feature Fusion...

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IEEE visual communications and image processing (VCIP)

作者： Birendra Kathariya Zhu Li Hongtao Wang Geert Van Der Auwera University of Missouri-Kansas City Kansas City USA Qualcomm San Diego USA

ISBN: (纸本)9781665475938

Versatile Video Coding (VVC)/H.266 is currently the state-of-the-art video coding standard with significant improvement in coding efficiency over its predecessor High Efficiency Video Coding (HEVC)/H.26S. Nonetheless, VVC is also block-based video coding technology where decoded pictures contain compression artifacts. In VVC, in-loop filters serve to suppress these compression artifacts. In this paper, convolution neural network (CNN) is utilized to better facilitate the suppression of compression artifacts over VVC. Nonetheless, our approach has uniqueness in obtaining better features by exploiting locally correlated spatial features in the pixel domain as well as long-range correlated spectral features in the discrete cosine transform (DCT) domain. In particular, we utilized CNN-features from DCT transformed input to extract high-frequency components and induce long-range correlation into the spatial CNN-features by employing multi-stage feature fusion. Our experimental result shows that the proposed approach achieves significant coding improvements up to 9.70% on average Bjantegaard Delta (BD)-Bitrate savings under AI configurations for luma (Y) components.

关键词： image coding Correlation visual communication Convolution Neural networks Transforms Feature extraction

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Memory-Efficient Point Cloud Registration via Overlapping Region Sampling

arXiv

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arXiv 2024年

作者： Shimada, Tomoyasu Murasaki, Kazuhiko Sato, Shogo Nishimura, Toshihiko Yoshida, Taiga Tanida, Ryuichi NTT Corporation Japan

Recent advances in deep learning have improved 3D point cloud registration but increased graphics processing unit (GPU) memory usage, often requiring preliminary sampling that reduces accuracy. We propose an overlapping region sampling method to reduce memory usage while maintaining accuracy. Our approach estimates the overlapping region and intensively samples from it, using a k-nearest-neighbor (kNN) based point compression mechanism with multi layer perceptron (MLP) and transformer architectures. Evaluations on 3DMatch and 3DLoMatch datasets show our method outperforms other sampling methods in registration recall, especially at lower GPU memory levels. For 3DMatch, we achieve 94% recall with 33% reduced memory usage, with greater advantages in 3DLoMatch. Our method enables efficient large-scale point cloud registration in resource-constrained environments, maintaining high accuracy while significantly reducing memory requirements. This paper is accepted for IEEE International conference on visual communications and image processing 20241 © 2024, CC BY.

关键词： Graphics processing unit

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