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image contrast enhancement method based on display and human visual system characteristics

APPLIED OPTICS 2019年第7期58卷 1813-1823页

作者： Chen, Guo Li, Li Jin, Weiqi Liu, Mingcong Shi, Feng Beijing Inst Technol MOE Key Lab Optoelect Imaging Technol & Syst Beijing 100081 Peoples R China Sci & Technol Low Light Level Night Vis Lab Xian 710065 Shaanxi Peoples R China

At the present time, there are many image contrast enhancement methods where the main considerations are detail enhancement, noise suppression, and high contrast suppression. Traditional methods ignore the characteristics of the display or merely consider the display as a whole. However, due to the limited dynamic range of most display devices on the market, the difference between two adjacent grayscales of the display is often below the just noticeable difference of the human visual systems, which causes many image details to be invisible on the display. To solve this problem, we present a preprocessing method for image contrast enhancement. The method combines the characteristics of the human eye and the display to enhance the image by examining the local histogram. When displaying the processed image, the algorithm maintains as much image information as possible, and image details will not be lost due to the limits of the display device. Moreover, this algorithm performs well for noise suppression and high contrast suppression. The algorithm is an image enhancement method and can also be a correction method for images enhanced by other methods when prepared for display. (C) 2019 Optical Society of America

关键词： image CONTRAST image information display devices high-contrast image enhancement method Noise suppression Refractive surgery enhancement image Visual System

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Advanced image enhancement method for mammogram analysis

Advanced image enhancement method for mammogram analysis

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International Conference on Recent Trends in Information Technology (ICRTIT)

作者： K. Nagaiah K. Manjunathachari T. V. Rajinikanth Dept of ECE JNTU Hyderabad Hyderabad Telangana India Professor and HOD Dept of ECE Gitam University Hyderabad Medak Telangana India Dept. Of CSE Sreenidhi Institute of Science and Technology Hyderabad Telangana India

ISBN: (纸本)9781509004331

In this paper we introduced the three image processing filtering methods. It will be useful for the detection of micro calcification in mammogram image analysis. The preprocessing method plays a vital role in automatic detection of breast cancer. The introduced three filtering methods are Median, Bilateral and the Inverse transform filter. The filter performance analyzed based on the results produced with the help of PSNR and MSE values. Experimental results found that the Inverse transform filter has given PSNR= 95.37, MSE= 0.00001 with noise density 0.005 for salt-and-pepper, Gaussian, Poisson and speckle noise. Then we can say that this filter is the one of the best filters for medical applications.

关键词： medical image processing cancer image enhancement image filtering inverse transforms mammography Digital Mammography image filtering Medical image processing image enhancement image enhancement method Laplace's equation Filterability Filters Peak signal-to-noise ratio Cancer Breast Cancer automatic detection Mean square error

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A new seismic image enhancement method based on mathematical morphology

A new seismic image enhancement method based on mathematical...

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11th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM 2015)

作者： Haixia Pan Zhongyan Wang Ting Zhang IRIP Lab School of Software Beihang University Beijing China

ISBN: (纸本)9781510821279

Seismic images are collected by seismic sensors. Containing the geological information of geological structures such as strata and crater, these images are of high significance. However, due to the complexity of their backgrounds, edges of structures to be studied appear blurry. This paper proposes a method to enhance the strata and crater structures using morphology. Opening and closing operations are used to enhance crater and multiscale morphological gradient is employed to show the strata. Experiment's result shows that this method performs well in both efficiency and effectiveness.

关键词： Mathematical Morphology Multiscale Morphological Gradient Seismic image mathematical morphology image enhancement method STRATA craters Earthquakes seismic methods

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Edge preservation image enlargement and enhancement method based on the adaptive Perona-Malik non-linear diffusion model

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IET image PROCESSING 2014年第12期8卷 753-760页

作者： Maiseli, Baraka Elisha, Ogada Mei, Jiangyuan Gao, Huijun Harbin Inst Technol Res Inst Intelligent Control & Syst Dept Control Sci & Engn Harbin 150001 Peoples R China Univ Dar Es Salaam Coll Informat & Commun Technol Dept Elect & Telecommun Engn Dar Es Salaam Tanzania Harbin Inst Technol Dept Math Harbin 150001 Peoples R China Egerton Univ Dept Math Egerton Kenya

In this study, the authors have proposed a new super resolution (SR) model based on the Perona-Malik regularisation scheme. The new model integrates into its regularisation component an adaptive exponential term which automatically adjusts itself depending on the local image features. This lends more sensitivity and adaptability to the proposed model, thereby making the reconstruction process much less punishing against semantically important features. Therefore, regularisation is stronger in homogeneous regions, and weaker in the neighbourhood of boundaries. The proposed method has a promising capability of supressing noise more effectively, while preserving important image features. The approach used differs significantly from the available methods, especially in the manner in which adaptability has been deployed. Noting that SR methods are less sensitive to the local image topography, a factor that causes the super-resolved images to be visually poor, the new method sensitively probes the local features of the image, and determines the necessary level of reconstruction and regularisation. Additionally, the formulation robustly introduces a backward diffusion, a phenomenon proved from literature to have a tendency of sharpening edges. The authors have included empirical reconstruction results to demonstrate that their model produces better images in comparison with other classical methods.

关键词： edge detection feature extraction image enhancement image resolution diffusion image reconstruction edge preservation image enlargement image enhancement method adaptive Peronaâ€“Malik nonlinear diffusion model superresolution model diffusivity component regularisation kernel SR model homogeneous region image resolution image topography image reconstruction feature distortion edge sharpening

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Performance of image enhancement methods for Diabetic Retinopathy based on Retinal Fundus image

Performance of Image Enhancement Methods for Diabetic Retino...

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International Conference on Computer Applications and Industrial Electronics (ICCAIE)

作者： Nurul Atikah Binti Mohd Sharif Nor Hazlyna Binti Harun Yuhanis Binti Yusof Zunaina Embong Juhaida Binti Abu Bakar Muhammad Khusairi Osman Nurul Fatin Shazzwanie Shamsudin Lim Xiao Feng School of Computing Universiti Utara Malaysia Sintok Kedah Ophthalmology Universiti Sains Malaysia Kubang Kerian Kelantan Faculty of Electrical Engineering Universiti Teknologi MARA Penang Branch Permatang Pauh Penang

ISBN: (数字)9781728150338

ISBN: (纸本)9781728150345

In Malaysia, Non-mydriatic fundus camera become a primary tool for Diabetic Retinopathy screening protocols due to user friendly and cost effective procedure. However, the quality of fundus image produces often suffer from uneven illumination, color distortion, blur, and low contrast. Therefore, the need for image enhancement become crucial to be implemented as a pre-processing technique in image processing funnel. This paper presents six general basic methods that commonly applied for image enhancement which includes histogram equalization, contrast stretching, image negative, brightness enhances, low light image and gray level slicing. The performance evaluation of each method compared based on human interpretations and quantitative measurement using MSE, PSNR and entropy. Retinal fundus images collected from Ophthalmology Clinic, Hospital Universiti Sains Malaysia were used as the input images. Quantitative and qualitative result shows that CS method become the preferred method to be used for image enhancement of retinal fundus image in Diabetic Retinopathy.

关键词： image enhancement diabetic retinopathy image enhancement Diabetic Retinopathy image enhancement method images image imagery (Psychotherapy) Fundus camera Retinaldehyde

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Breast mass detection in digital mammography based on anchor-free architecture

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COMPUTER methodS AND PROGRAMS IN BIOMEDICINE 2021年 205卷 106033-106033页

作者： Cao, Haichao Pu, Shiliang Tan, Wenming Tong, Junyan Hikvis Digital Technol Co Ltd Hangzhou 310051 Peoples R China

Background and objective: Accurate detection of breast masses in mammography images is critical to di-agnose early breast cancer, which can greatly improve the patients' survival rate. However, it is still a big challenge due to the heterogeneity of breast masses and the complexity of their surrounding environ-ment. Therefore, how to develop a robust breast mass detection framework in clinical practical applica-tions to improve patient survival is a topic that researchers need to continue to explore. methods: To address these problems, we propose a one-stage object detection architecture, called Breast Mass Detection Network (BMassDNet), based on anchor-free and feature pyramid which makes the de-tection of breast masses of different sizes well adapted. We introduce a truncation normalization method combine it with adaptive histogram equalization to enhance the contrast between the breast mass the surrounding environment. Meanwhile, to solve the overfitting problem caused by small data size, propose a natural deformation data augmentation method and mend the train data dynamic updat-method based on the data complexity to effectively utilize the limited data. Finally, we use transfer learning to assist the training process and to improve the robustness of the model ulteriorly. Results: On the INbreast dataset, each image has an average of 0.495 false positives whilst the recall rate 0.930;On the DDSM dataset, when each image has 0.599 false positives, the recall rate reaches 0.943. Conclusions: The experimental results on datasets INbreast and DDSM show that the proposed BMassD-can obtain competitive detection performance over the current top ranked methods. (c) 2021 Published by Elsevier B.V.

关键词： Breast mass detection Anchor-free architecture image enhancement method Data augmentation method Training method

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Color image enhancement of Acute Leukemia Cells in Blood Microscopic image for Leukemia Detection Sample

Color Image Enhancement of Acute Leukemia Cells in Blood Mic...

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International Conference on Computer Applications and Industrial Electronics (ICCAIE)

作者： Nor Hazlyna Harun Juhaida Abu Bakar Zulkifli Abd Wahab Muhammad Khusairi Osman Hazaruddin Harun School of Computing Universiti Utara Malaysia Kedah Malaysia Faculty of Electrical Engineering Universiti Teknologi MARA Penang Malaysia

ISBN: (数字)9781728150338

ISBN: (纸本)9781728150345

Leukemia is a type of cancer that affects the white blood cell. Early detection of leukemia is important to reduce the rate of mortality. In order to detect acute leukemia, conventional screening method based on microscopic image is used, where sample of blood cell will be taken from the suspected leukemia patient and manually white blood cell (WBC) condition is observed using microscope. The manual screening process is tedious, time consuming and usually prone to error due to low contrast between the nucleus and cytoplasm of WBCs. This report introduces a new enhancement method which is a combination of Particle swarm optimization (PSO) algorithm and contrast stretching, known as Hybrid PSO-Contrast stretching (HPSO-CS). The PSO has been used to optimize the fitness criterion in order to improve the contrast and detail in microscopic image by adapting the parameters as a contribution to enhancement technique. In this study, PSO algorithm is used to perform image segmentation to remove all the unwanted part such as red blood cell (RBC), platelet and also the background while retain the WBC part. The segmentation algorithm uses saturation S-component based on Hue, Saturation, Intensity (HSI) color model. After the segmentation is done, contrast stretching process is applied to the original image to stretch intensity of the pixel. Then the segmented image is combined with the resultant image that has been stretched to produce the enhanced image. The results of the proposed method are evaluated by using mean-square error (MSE), Peak-signal-to-noise-ratio (PSNR) and Absolute mean brightness error (AMBE). This proposed method is benchmarked by comparing against two image enhancement methods, global enhancement and Class Limited Adaptive Histogram Equalization (CLAHE). Based on the results, it can be concluded that quality of the enhanced image for the proposed method is much better with the lowest MSE (2067.651), AMBE (43.51827) and highest PSNR (14.98671) compared to th

关键词： leukemia disease image enhancement particle swarm optimization (PSO) contrast stretching microprojection image enhancement Leukemia Leukocytes Leukemic Cell image enhancement method Blood Cells hematologists Acute Leukemia parametric subharmonic oscillator Refractive surgery enhancement Mortality

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An Algorithm for Fabric Defect Detection Based on Adaptive Canny Operator

An Algorithm for Fabric Defect Detection Based on Adaptive C...

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International Conference on Intelligent Computing, Automation and Systems (ICICAS)

作者： Ruijun Ma Sanpeng Deng Hongchang Sun Yuming Qi Tianjin University of Technology and Education Tianjin China

ISBN: (数字)9781728161068

ISBN: (纸本)9781728161075

Aiming at the problem that the parameters of traditional Canny operator need to be set manually and the adaptability of the algorithm is poor, this paper proposes an improved Canny operator algorithm for weaving damage detection. Firstly, the detection principle of traditional Canny operator is analyzed; secondly, the image processing of knitted fabric is improved by iterative filtering and single-scale Retinex (Retina and Cortex abbreviation) non-linear image enhancement method; then, the high and low thresholds of Canny operator are adaptively set by the method of maximum inter-class variance; finally, the algorithm is obtained by field experiments and the accuracy rate is 0.97. Compared with the traditional Canny operator, it has better adaptability and detection accuracy. At the same time, it can describe the details of knitted fabric defects better.

关键词： adaptive filters fabrics image enhancement iterative methods object detection weaving Canny edge detector Weaving adaptive filters Object detection image enhancement Textile fabrics iterative methods fabric fabric defect accuracy rate image enhancement method

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image enhancement for outdoor long-range surveillance using IQ-learning multiscale Retinex

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IET image PROCESSING 2017年第9期11卷 786-795页

作者： Liu, Haoting Lu, Hanqing Zhang, Yu Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing Peoples R China Astronaut Res & Training Ctr China Beijing Peoples R China

The visible light camera-based long-range surveillance always suffers from the complex atmosphere. When applying some traditional image enhancement methods, the computational effects behave limited because of their poor environment adaptability. To conquer that problem, a blind image quality (IQ) learning-based multiscale Retinex, i.e. the IQ-learning multiscale Retinex, is proposed. First, a series of typical degenerated images are collected. Second, several blind IQ evaluation metrics are computed for the dataset above. They are the image brightness degree, the image region contrast degree, the image edge blur degree, the image colour quality degree, and the image noise degree. Third, a wavelet transform multi-scale Retinex (WT_MSR) is used to carry out the basic image enhancement. A kind of optimal enhancement is implemented by the subjective evaluation and tuning of multiple optimal control parameters (MOCPs) of WT_MSR for these degenerated dataset. Fourth, the back propagation neural network (BPNN) is used to build a connection between the IQ metrics and the MOCPs. Finally, when a new image is captured, this system will compute its IQ metrics and estimate the MOCPs for the WT_MSR by BPNN;then a kind of optimal enhancement can be realised. Many outdoor applications have shown the effectiveness of proposed method.

关键词： image enhancement video surveillance image restoration wavelet transforms neural nets backpropagation outdoor long-range surveillance blind IQ-learning multiscale Retinex visible light camera-based image enhancement method blind image quality learning multiscale Retinex image brightness degree image region contrast degree image edge blur degree image colour quality degree image noise degree wavelet transform multiscale Retinex WT_MSR multiple optimal control parameter MOCP backpropagation neural network BPNN

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image enhancement with Gaussian filtering in time-domain microwave imaging system for breast cancer detection

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ELECTRONICS LETTERS 2016年第5期52卷 342-343页

作者： Kwon, S. Lee, H. Lee, S. Ewha Womans Univ Dept Elect Engn 11-1 Daehyun Dong Seoul 120750 South Korea

A time-domain microwave imaging system with improved image quality is presented for early detection of breast cancer. The time-domain system has the advantage of short scan time compared with the frequency-domain system but suffers from low signal-to-noise ratio (SNR) due to high attenuation of the RF signal within the breast tissue. Signal averaging of repeated measurements can improve the SNR, but this method also smears the signal peaks due to the signal-averaged jitter, which adversely affects the reconstructed image quality. An image enhancement method using Gaussian band-pass filtering (BPF) is proposed for a time-domain imaging system. The results from the proposed method were compared with those from the deconvolution method, which is typically used to remove the effects of signal-averaged timing jitter in short pulse measurements. Simulation results with a 3D hemispherical homogeneous breast model containing tumour cells revealed improvement of the reconstructed image quality with the proposed method in the presence of timing jitter. The signal-to-mean ratio was increased from 6.46 to 14.15 with BPF, whereas deconvolution resulted in an increase to 9.36. Simulation results with a non-homogeneous realistic breast model also showed image enhancement with the proposed method.

关键词： Gaussian processes band-pass filters image enhancement image reconstruction medical image processing time-domain analysis 3D hemispherical homogeneous breast model BPF Gaussian bandpass filtering breast cancer breast cancer detection breast tissue image enhancement method improved image quality signal-averaged timing jitter time-domain microwave imaging system tumour cells

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