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Self-adaptive image processing using blind image quality assessment technique

Perspectives in Science 2016年 8卷 639-641页

作者： K.S. Prasada Kumari Department of Electronics Engineering JSS Academy of Technical Education C-20/1 Sector 62 Noida UP 201301 India

image processing technique for filtering noise is a major challenge for DSP engineers. When the images are corrupted by noise whose characteristics cannot be evaluated a priori, processing systems need to be flexible and adaptable. General-purpose filters based on assumptions about image noise models fail to meet the quality and performance criteria in dealing with unmodelled noise. At the same time, evolutionary algorithms based adaptable filter architecture is proved to be successful in this regard. While existing evolutionary techniques based designs use uncorrupted reference image and compute mean absolute error for evolving a noise filter, the paper proposes a novel noise quality index based technique. The proposed entropy based scheme estimates noise content without any reference image and such a system is vital in situations where uncorrupted image reference is unavailable. Based on experimental results, the paper compares no-reference image noise assessment techniques with reference based technique and concludes that the proposed blind noise assessment method is accurate as referenced based techniques. From implementation point of view, the no-reference scheme is computationally intensive.

关键词： Noise model Entropy image quality Evolutionary algorithm Fitness value

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Brain MRI image Segmentation Based on Improved Fuzzy C-Means Algorithm

Brain MRI Image Segmentation Based on Improved Fuzzy C-Means...

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International Conference on Smart City and systems Engineering (ICSCSE)

作者： Shiling Sun Shuxun Yan Ying Wang Yun Li Henan College of Traditional Chinese Medicine Zhengzhou Henan China The First Affiliated Hospital Zhengzhou University Zhengzhou Henan China

ISBN: (纸本)9781509055319

image segmentation is a challenge and difficult task in image processing, and the foundation of image analysis and identifying. This paper mainly studies the means clustering image segmentation. In view of the traditional clustering image segmentation algorithm for image segmentation accuracy is low problem, put forward a kind of fuzzy control based on C-means clustering image segmentation method. Methods firstly in clustering image segmentation algorithm based on fast, using fuzzy C-means clustering algorithm for image segmentation. The experimental results show that the algorithm in clustering, to optimize the performance of the same premise, image segmentation edge clear, segmentation better than traditional clustering algorithm for image segmentation.

关键词： image segmentation Clustering algorithms Algorithm design and analysis Magnetic resonance imaging image edge detection Brain modeling

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Road sign recognition system on Raspberry Pi

Road sign recognition system on Raspberry Pi

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IEEE Long Island systems, Applications and Technology Conference (LISAT)

作者： Enis Bilgin Stefan Robila Department of Computer Science Montclair State University Montclair NJ USA

ISBN: (纸本)9781467384919

Digital image processing, i.e. the use of computer systems to process pictures, has applications in many fields, including of medicine, space exploration, geology and oceanography and continues to increase in its applicability. The main objective of this paper is to demonstrate the ability of image processing algorithms on a small computing platform. Specifically we created a road sign recognition system based on an embedded system that reads and recognizes speed signs. The paper describes the characteristics of speed signs, requirements and difficulties behind implementing a real-time base system with embedded system, and how to deal with numbers using image processing techniques based on shape and dimension analysis. The paper also shows the techniques used for classification and recognition. Color analysis also plays a specifically important role in many other different applications for road sign detection, this paper points to many problems regarding stability of color detection due to daylight conditions, so absence of color model can led a better solution. In this project lightweight techniques were mainly used due to limitation of real- time based application and Raspberry Pi capabilities. Raspberry Pi is the main target for the implementation, as it provides an interface between sensors, database, and image processing results, while also performing functions to manipulate peripheral units (usb dongle, keyboard etc.).

关键词： Optical character recognition software Shape Character recognition Engines image recognition Cameras

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MULTI-image SUPER-RESOLUTION USING A DUAL WEIGHTING SCHEME BASED ON VORONOI TESSELLATION

MULTI-IMAGE SUPER-RESOLUTION USING A DUAL WEIGHTING SCHEME B...

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IEEE International Conference on image processing

作者： Michel Batz Andrea Eichenseer Andre Kaup Multimedia Communications and Signal Processing Friedrich-Alexander University Erlangen-Niirnberg (FAU)

ISBN: (纸本)9781467399623

Increasing spatial resolution is often required in many applications such as entertainment systems or video surveillance. Apart from using higher resolution sensors, it is also possible to apply super-resolution algorithms to realize an increased resolution. Those methods can be divided into approaches that rely on only a single low resolution image or on multiple low resolution video frames. While incorporating more frames into the super-resolution is beneficial for the resolution enhancement in principle, it is also likely to introduce more artifacts from inaccurate motion estimation. To alleviate this problem, various weightings have been proposed in the literature. In this paper, we propose an extended dual weighting scheme for an interpolation-based super-resolution method based on Voronoi tessellation that relies on both a motion confidence weight and a distance weight. Compared to non-weighted super-resolution, the proposed method yields an average gain in luminance PSNR of up to 1.29 dB and 0.61 dB for upscaling factors of 2 and 4, respectively. Visual comparisons substantiate the objective results.

关键词： Resolution Enhancement image processing Multi-image Super-Resolution Voronoi Tessellation

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Automatic Detection of Laser Marks in Retinal Digital Fundus images

Automatic Detection of Laser Marks in Retinal Digital Fundus...

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European Signal processing Conference

作者： Joao G. R. Almeida e Sousa Carlos Manta Oliveira Luis A. da Silva Cruz Department of Physics University of Coimbra Retmarker S.A. Instituto de Telecomunicacoes Department of Electrical and Computer Engineering University of Coimbra

ISBN: (纸本)9781509018918

Diabetic retinopathy (DR) is the most frequent complication of diabetes mellitus that affects vision to the point of causing blindness. In advanced stages its progress can be delayed with laser photocoagulation which leaves behind marks on the retina. Modern screening programs rely on automatic diagnostic algorithms to detect signs of DR in patients. These systems performance may be impaired when patient retina presents marks from previous laser photocoagulation treatments. Since these patients are already being treated, it is desirable to detect and remove them from the screening program. An algorithm that automatically detects the presence of laser marks in retinal images using tree-based classifiers is proposed and the results on its performance are obtained and described. Two new public accessible datasets containing retinal images with laser marks are provided in this paper.

关键词： Diabetes Biomedical image processing Feature extraction Classification algorithms Medical image processing Sorting algorithm Feature extraction Diabetes Mellitus automatic detection Laser Coagulation System performance Retinaldehyde DR Complications of Diabetes Mellitus Retinal image images

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Colour constancy using sub-blocks of the image

Colour constancy using sub-blocks of the image

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International Conference on Signals and Electronic systems, ICSES

作者： Md. Akmol Hussain Akbar Sheikh Akbari Bruhanth Mallik School of Computing Leeds Beckett University Leeds UK

Colour constancy is the ability to measure the colour of objects independent of the light source, while colour casting is the presence of unwanted colour in digital images. Colour casting significantly affects the performance of image processing algorithms such as image segmentation and object recognition. The presence of large uniform background within the image considerably deteriorates the performance of many state of the art colour constancy algorithms. This paper presents a colour constancy method using the sub-blocks of the image to alleviate the effect of large uniform colour area of the scene. The proposed method divides the input image into a number of non-overlapping blocks, and Average Absolute Difference (AAD) value of each block colour component is calculated. The blocks with AAD greater than threshold values, which are empirically determined for each colour component, are considered to have sufficient colour information. The selected blocks are then used to determine the scaling factors to achieve achromatic values for the input image colour components. Comparing the performance of the proposed technique with the state of the art methods using images from three datasets shows that the proposed method outperforms the state of the art techniques in the presence of large uniform colour patches.

关键词： image color analysis image edge detection Cameras Speech recognition Light sources Algorithm design and analysis

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A hybrid multiple exposure image fusion approach for HDR image synthesis

A hybrid multiple exposure image fusion approach for HDR ima...

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IEEE International Workshop on Imaging systems and Techniques (IST)

作者： Ioannis Merianos Nikolaos Mitianoudis Electrical and Computer Engineering Dep. Democritus University of Thrace Xanthi Greece

ISBN: (纸本)9781509018185

The latest advancement in imaging applications has increased the need for more High Definition Range (HDR) imaging, which is not easily attainable by common imaging sensors. However, the use of multiple exposure images, that cover multiple exposure settings for the captured scene, and their combination in a single image via image fusion has been proposed in the literature and seems a viable solution. In this paper, the authors combine two image fusion methods to perform multiple exposure fusion. They use Mitianoudis and Stathaki [1] method to fuse the luminance channel and the Mertens et al [2] method to fuse the color channels. The derived fusion output outperforms both individual methods and other state-of-the-art methods.

关键词： image color analysis image fusion Signal processing algorithms Fuses Cameras Dynamic range

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Effects of color spaces to the cell segmentation in histopathological images

Effects of color spaces to the cell segmentation in histopat...

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IEEE Signal processing and Communications Applications (SIU)

作者： Abdulkadir Albayrak Gokhan Bilgin Bilgisayar Mühendisliği Bölümü Yıldız Teknik Üniversitesi 34220 İstanbul Türkiye

With the progressive development of the computer-aided diagnosis (CAD) systems, analysis of high resolution histopathological images has become more easier. In the proposed study, the effects of different color spaces used in various studies in the literature are investigated for the discrimination of cellular structures from background in histopathological images. For this purpose, performances of k-means, fuzzy c-means and expectation-maximization algorithms are compared in different color spaces. In the experimental results section, different segmentation accuracy metrics are presented in comparative manner.

关键词： image segmentation image color analysis Pathology image analysis Active contours Biomedical imaging Conferences

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GPU implementation of multi-scale Retinex image enhancement algorithm

GPU implementation of multi-scale Retinex image enhancement ...

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ACS/IEEE International Conference on Computer systems and Applications

作者： Hui Li Weihao Xie Xingang Wang Shousheng Liu Yingying Gai Lei Yang Institute of Oceanographic Instrumentation Shandong Academy of Science Qingdao China

Multi-scale Retinex algorithm is an image enhancement algorithm that aims at image reconstruction. The algorithm maintains the high fidelity and the dynamic range compression of the image, so the enhancement effect is obvious. The algorithm exploits a large number of convolution operations to achieve dynamic range compression and color/brightness rendition, and the calculation time increased significantly with the increase of the image resolution. In order to improve the real-time performance of the algorithm, a multi-scale Retinex image enhancement algorithm based on GPU CUDA is proposed in this paper. Through the data mining and parallel analysis of the algorithm, time-consuming modules of the calculation, such as Gauss filter, convolution, logarithm difference, are implemented in GPU by exploiting the massively parallel threading and heterogeneous memory hierarchy of GPGPU to improve efficiency. The experimental results show that the algorithm can improve the computing speed significantly in NVIDIA Tesla K20 and CUDA7.5, and with the increase of image resolution, the maximum speedup can reach 202 times.

关键词： Graphics processing units Instruction sets Convolution Algorithm design and analysis Parallel processing Heuristic algorithms Kernel

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Multi-level parallelism analysis and system-level simulation for many-core Vision processor design

Multi-level parallelism analysis and system-level simulation...

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Mediterranean Conference on Embedded Computing (MECO)

作者： Jones Y. Mori Michael Hübner Department of Mechanical Engineering University of Brasilia Brazil Chair of Embedded Systems for Information Technology (ESIT) Ruhr-University Bochum Germany

The technology convergence and the evolution of embedded systems to multi/many-core architectures allow envisioning future cameras as many-core systems able to process complex image processing and Computer Vision (IP/CV) applications. IP/CV algorithms have natural parallelism which must be efficiently explored to meet embedded application's constraints (real-time, power consumption, silicon area, temperature management, fault tolerance, and so on). In the case of many-core architectures, the efficiency comes not only from the number and type of processing cores but how they communicate and how the memory is organized. In this work, we show a multi-level parallelism study of IP/CV algorithms/applications, analyzing how to explore the different features available in many-core architecture's design space. The analysis is performed using a high-level SystemC/TLM2.0 platform specially developed for this task. As results, we propose a hierarchical parallelism extraction, a transparent programming model and a many-core architecture template for the next generation of vision processors.

关键词： Parallel processing image edge detection Program processors Programming Computer architecture Algorithm design and analysis Space exploration

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