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Underwater image Enhancement with a Total Generalized Variation Illumination Prior

Underwater Image Enhancement with a Total Generalized Variat...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Zhengjie Zhao Yuxiang Dai Peixian Zhuang School of Electronic and Information Engineering Nanjing University of Information Science and Technology Nanjing China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

In order to promote the visual quality of degraded underwater images, we develop an innovative variational model based on Retinex with a total generalized variation (TGV) prior on the illumination. The TGV prior is adopted to approximate piecewise smoothness and piecewise linear smoothness of the illumination, which combines the first-order and second-order total variation (TV) to model the variation of illumination. When adopting this illumination prior in the Retinex-based algorithm, the illumination and reflection are well separated, and underwater enhanced results appear more natural and their details and edges are better preserved. Then an efficient iterative optimization method is derived to settle the proposed model via alternately calculating the illumination and the reflection simultaneously. Numerous experiments on both visual results and objective metrics demonstrate the superiority of our method compared with several underwater enhancement methods. In addition, the proposed method can be extended for dehazing, sandstorm removal and low illumination image enhancement, which can illustrate better capacity of our model.

关键词： Lighting image color analysis Mathematical model Optimization image enhancement visualization Adaptation models

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Medical image Denoising Using Synergistic Fibroblast Optimization Based Weighted Median Filter 2

Medical Image Denoising Using Synergistic Fibroblast Optimiz...

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2nd International conference on Advances in Electronics, Computers and communications (ICAECC)

作者： Dhivyaprabha, T. T. Jayashree, G. Subashini, P. Avinashilingam Inst Home Sci & Higher Educ Women Dept Comp Sci Coimbatore Tamil Nadu India Kumaraguru Coll Technol Dept Informat Technol Coimbatore Tamil Nadu India

ISBN: (纸本)9781538637852

image analysis involves processing and extraction of knowledge from images which is significantly useful in the large scale of medical and engineering applications. image denoising is primarily applied in image analysis for degradation of noise, and thus improves the visual quality of images for information retrieval process. In the recent scenarios, due to the increase of complexity and diversity of digital images, removal of noise present in complicated images using classical filters becomes a quite challenge. The objective of this paper is to investigate the performance efficiency of a newly developed Synergistic Fibroblast Optimization based Weighted Median Filter (SFO-WMF) for medical image analysis. Experiments are carried out with benchmark images, real time Magnetic Resonance Imaging (MRI) images, ultrasound breast cancer images and compared with conventional filters, namely, mean filter, median filter, wiener filter, Gaussian filter and weighted median filter. The performances of filters are validated using standard performance metrics and computational results demonstrated that the novel filter produces promising results and it outperforms than conventional filters in both qualitative and quantitative perspectives.

关键词： denoising filtering technique medical image analysis MRI dataset Synergistic Fibroblast Optimization (SFO) ultrasound dataset

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Meaning and Uncertainty Inherent in Understanding images, Spatial-Taxon Hierarchy, Word Annotation and Relevant Context 17th

Meaning and Uncertainty Inherent in Understanding Images, Sp...

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17th International conference on Information processing and Management of Uncertainty in Knowledge-Based Systems (IPMU)

作者： Barghout, Lauren Univ Calif Berkeley Elect Engn & Comp Sci BISC Berkeley CA 94720 USA

ISBN: (纸本)9783319914794;9783319914787

This paper explores the meaning and uncertainty inherent in (a) understanding image hierarchies;(b) describing them with words;and (c) navigating the abstraction context of the viewer. A spatial-taxon hierarchy, a standardized scene architecture, partitions an image into a foreground, subject and salient objects and/or sub-objects. The introduction starts with a thought experiment (Thought experiments, borrowed from the model-theoretic isomorphism standard of structure-mapping theory, enable readers to compare two systems thought to be similar. It's a form of inductive reasoning that expands knowledge in the face of uncertainty (Holland et al. 1986 [13]) by providing an explicit representation of how two systems are similar. Though the conclusion that the two systems do share an isomorphic structure can only be supported via various degrees of truth (fuzzy membership), it establishes its plausibility. Analogical reasoning is natural to human thought and communication making it useful for scientific papers.) based on a poem & an image landscape. The thought experiment is intended to provide analogical inference as scaffolding for the rest of the paper. The results of experimental data of human annotated spatial-taxon and corresponding word descriptions of two images are presented. The experimental results are analyzed in terms of spatial-taxon designation and the meaning & uncertainty presented by the human annotations. The results support the fuzzy spatial-taxon hierarchy of human scene perception described by other works, show that word descriptions depend on spatial-taxon designation and that long tail word distributions require unbounded possibility with semantic uncertainty (type 2 fuzzy sets) for the word counts in the probability distribution. Deep learning image recognition, Zadeh information restriction principal, Shannon's distinction between information content and semantics, customized image descriptions and fuzzy inference techniques are explored.

关键词： Segmentation Recognition Classification image labeling Semantic Spatial-taxon Fuzzy inference Possibility theory Deep learning Information theory visual-taxometrics Machine vision Human vision Attention visual & image cognition

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visual Object Networks: image Generation with Disentangled 3D Representation 32

Visual Object Networks: Image Generation with Disentangled 3...

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32nd conference on Neural Information processing Systems (NIPS)

作者： Zhu, Jun-Yan Zhang, Zhoutong Zhang, Chengkai Wu, Jiajun Torralba, Antonio Tenenbaum, Joshua B. Freeman, William T. MIT CSAIL Cambridge MA 02139 USA MIT CSAIL Google Cambridge MA 02139 USA

Recent progress in deep generative models has led to tremendous breakthroughs in image generation. However, while existing models can synthesize photorealistic images, they lack an understanding of our underlying 3D world. We present a new generative model, visual Object Networks (VON), synthesizing natural images of objects with a disentangled 3D representation. Inspired by classic graphics rendering pipelines, we unravel our image formation process into three conditionally independent factors-shape, viewpoint, and texture-and present an end-to-end adversarial learning framework that jointly models 3D shapes and 2D images. Our model first learns to synthesize 3D shapes that are indistinguishable from real shapes. It then renders the object's 2.5D sketches (i.e., silhouette and depth map) from its shape under a sampled viewpoint. Finally, it learns to add realistic texture to these 2.5D sketches to generate natural images. The VON not only generates images that are more realistic than state-of-the-art 2D image synthesis methods, but also enables many 3D operations such as changing the viewpoint of a generated image, editing of shape and texture, linear interpolation in texture and shape space, and transferring appearance across different objects and viewpoints.

关键词： Textures

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image Analysis in Light Sheet Fluorescence Microscopy images of Transgenic Zebrafish Vascular Development 22nd

Image Analysis in Light Sheet Fluorescence Microscopy Images...

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22nd conference on Medical image Understanding and Analysis (MIUA)

作者： Kugler, Elisabeth Chico, Timothy Armitage, Paul Univ Sheffield Dept Infect Immun & Cardiovasc Dis Fac Med Sheffield S10 2TN S Yorkshire England Univ Sheffield Bateson Ctr Sheffield S10 2TN S Yorkshire England

ISBN: (纸本)9783319959214;9783319959207

The zebrafish has become an established model to study vascular development and disease in vivo. However, despite it now being possible to acquire high-resolution data with state-of-the-art fluorescence microscopy, such as lightsheet microscopy, most data interpretation in pre-clinical neurovascular research relies on visual subjective judgement, rather than objective quantification. Therefore, we describe the development of an image analysis workflow towards the quantification and description of zebrafish neurovascular development. In this paper we focus on data acquisition by lightsheet fluorescence microscopy, data properties, image pre-processing, and vasculature segmentation, and propose future work to derive quantifications of zebrafish neurovasculature development.

关键词： 3D Analysis Development In vivo Light sheet fluorescence microscopy (LSFM) Segmentation Vasculature Zebrafish

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Improved ORB Algorithm Based on Binocular Vision

Improved ORB Algorithm Based on Binocular Vision

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IEEE International conference on Computer and communications

作者： Chao Li Yongxing Jia Huali Wang Chuanzhen Rong Ying Zhu Coege of Counicions Engineering Ary Engineering Universiy of PLACoege of Counicions Engineering Ary Engineering Universiy of PLACoege of Counicions Engineering Ary Engineering Universiy of PLACoege of Counicions Engineering Ary Engineering Universiy of PLACoege of Counicions Engineering Ary Engineering Universiy of PLA

ISBN: (数字)9781728147437

ISBN: (纸本)9781728147444

In digital image processing, feature extraction is a very important step, and ORB algorithm is the best one in realtime performance, but it is not up to the standard in the system with higher real-time requirements. Through binocular vision calibration, we will delve into how to speed up the operation of ORB algorithm with ensuring accuracy. First, the overlapping regions of the left and right images are extracted, and then the images are subjected to morphological operations, and corner detection is performed at the edges. Secondly, the original description method is improved, and the description sub-selects the window value based on the area ring, which is more in line with the characteristics of the human eye and improves the operation speed. Then, a hierarchical matching strategy is adopted, combined with binocular visual constraints, the matching range is defined, and the accuracy is effectively improved. Finally, construct an experimental platform to process two images and compare them with existing algorithms. The experimental results show that the proposed method has much less computational time consumption than the original algorithm under the condition of improved accuracy, and is suitable for fast matching environment.

关键词： Feature extraction image edge detection Real-time systems Calibration Cameras Morphological operations Robustness

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Single noisy image super resolution by minimizing nuclear norm in virtual sparse domain 6th

Single noisy image super resolution by minimizing nuclear no...

引用

6th National conference on Computer Vision, Pattern Recognition, image processing and Graphics, NCVPRIPG 2017

作者： Mandal, Srimanta Rajagopalan, A.N. Image Processing and Computer Vision Lab Department of Electrical Engineering IIT Madras Chennai600036 India

ISBN: (纸本)9789811300196

Super-resolving a noisy image is a challenging problem, and needs special care as compared to the conventional super resolution approaches, when the power of noise is unknown. In this scenario, we propose an approach to super-resolve single noisy image by minimizing nuclear norm in a virtual sparse domain that tunes with the power of noise via parameter learning. The approach minimizes nuclear norm to explore the inherent low-rank structure of visual data, and is further augmented with coarse-to-fine information by adaptively re-aligning the data along the principal components of a dictionary in virtual sparse domain. The experimental results demonstrate the robustness of our approach across different powers of noise. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Optical resolving power

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SAR image De-noising Based on Nuclear Norm Minimization Fusion Algorithm 5th

SAR Image De-noising Based on Nuclear Norm Minimization Fusi...

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5th International conference on communications, Signal processing, and Systems (CSPS)

作者： Liu, Shuaiqi Ming, Liu Shi, Mingzhu Qi, Xin Qi, Hu Hebei Univ Coll Elect & Informat Engn Baoding Peoples R China Key Lab Digital Med Engn Hebei Prov Baoding Peoples R China Hebei Univ Dept Personnel Baoding Peoples R China Tianjin Normal Univ Coll Elect & Commun Engn Tianjin Peoples R China

ISBN: (纸本)9789811032295;9789811032288

Synthetic aperture radar (SAR) images play a quite important role in military and environmental monitoring. But the SAR image was greatly affected by coherent noise, which affects its application in the subsequent image analysis. In most of the SAR image de-noising algorithms in hand, the same operation is applied to the whole SAR image, which leads to artificial texture or edge blur. In order to overcome this shortcoming, this paper proposed a new SAR image de-noising method based on nuclear norm minimization (NNM) fusion algorithm. The noisy SAR image is de-noised by two different algorithms, and two de-noising images are fused to final de-noising image based on nuclear norm minimization fusion algorithm. Experimental results show that the proposed algorithm not only effectively improves the visual effect and objective indicators of de-noising image but preserves the local structure of the image better.

关键词： image enhancement

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DnM3Net: Multi-Scale & Multi-Level Shuffle-CNN Via Multi-Level Attention for image Denoising

DnM3Net: Multi-Scale & Multi-Level Shuffle-CNN Via Multi-Lev...

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IEEE International Symposium on Parallel and Distributed processing with Applications and IEEE International conference on Ubiquitous Computing and communications (ISPA/IUCC)

作者： Yue Cao Jinhe He Yu Zhang Gang Lu Shigang Liu Xiaojun Wu School of Computer Science Shaanxi Normal University Xian Shaanxi Normal University Xi'an China

ISBN: (纸本)9781728152103

Recently, based on novel convolutional neural net-work architectures proposed, tremendous advances have been achieved in image denoising task. An effective and efficient multi-level network architecture for image denoising refers to restore the latent clean image from a coarser scale to finer scales and pass features through multiple levels of the model. Unfortunately, the bottleneck of applying multi-level network architecture lies in the multi-scale information from input images is not effectively captured and the fine-to-coarse feature fusion strategy to be ignored in image denoising task. To solve these problems, we propose a multi-scale & multi-level shuffle-CNN Via multi-level attention (DnM3Net), which plugs the multi-scale feature extraction, fine-to-coarse feature fusion strategy and multi-level attention module into the new network architecture in image denoising task. The advantage of this approach are two-fold: (1) It solve the multi-scale information extraction issue of multi-level network architecture, making it more effective and efficient for the image denoising task. (2) It is impressive performance because the better trade-off between denoising and detail preservation. The proposed novel network architecture is validated by applying on synthetic gaussian noise gray and RGB images. Experimental results show that the DnM3Net effectively improve the quantitative metrics and visual quality compared to the state-of-the-art denoising methods.

关键词： convolutional neural nets feature extraction Gaussian noise image colour analysis image denoising image sampling image denoising image samples image colour analysis NETWORK STRUCTURE Gaussian noise Feature extraction multi-scale hierarchical models Noise reduction

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Real-time lung segmentation from whole-body CT scans using Adaptive Vision Studio-a visual programming software suite

Real-time lung segmentation from whole-body CT scans using A...

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conference on Real-Time image and Video processing

作者： Nalepa, Jakub Czardybon, Michal Walczak, Maksym Future Proc Bojkowska 37A PL-44100 Gliwice Poland Silesian Tech Univ Akad 16 PL-44100 Gliwice Poland

ISBN: (数字)9781510618527

ISBN: (纸本)9781510618527

Computed tomography (CT) imaging became an indispensable modality exploited across a vast spectrum of clinical indications for diagnosis and follow-up, alongside various image-guided procedures, especially in patients with lung cancer. Accurate lung segmentation from whole-body CT scans is an initial, yet extremely important step in such procedures. Therefore, fast and robust (against low-quality data) segmentation techniques are being actively developed. In this paper, we propose a new real-time algorithm for segmenting lungs from the entire body CT scans. Our method benefits from both 2D and 3D analysis of CT images, coupled with several fast pruning strategies to remove false-positive tissue areas, including trachea and bronchi. Also, we developed a new approach for separating lungs which exploits spatial analysis of lung candidates. Our algorithms were implemented in Adaptive Vision Studio (AVS) a visual-programming software suite based on the data-flow paradigm. Although AVS is extensively used in machine-vision industrial applications (it is equipped with a range of highly optimized image-processing routines), we showed it can be easily utilized in general data analysis applications, including medical imaging. Experimental study performed on a benchmark dataset manually annotated by an experienced reader revealed that our algorithm is very fast (average processing time of an entire CT series is less than 1.5 seconds), and it is competitive against the state of the art, delivering high-quality and consistent results (DICE was above 0.97 for both lungs;0.96 for the left and 0.95 for the right lung after separation). The quantitative analysis was backed up with thorough qualitative investigation (including 2D and 3D visualizations) and statistical tests.

关键词： image segmentation image processing computed tomography lungs CAD

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