- 标题
- 摘要
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[IEEE 2018 25th IEEE International Conference on Image Processing (ICIP) - Athens, Greece (2018.10.7-2018.10.10)] 2018 25th IEEE International Conference on Image Processing (ICIP) - HUE-Preserving Color Contrast Enhancement Method Without Gamut Problem by Using Histogram Specification
摘要： Generally, in color image contrast enhancement, the intensity or saturation component of an image is processed without the change of hue component of the image. In some color space used for contrast enhancement, processed pixel values are located outside of the gamut of the RGB color space. In that case, a clipping process is required to make the pixel values located inside of the gamut. However, the clipping process changes the combination of R, G, and B components and causes unexpected hue changes. In the present paper, we propose a hue-preserving contrast enhancement method for color images without gamut problem. In the proposed method, the contrast enhancement of a color image is realized by spreading out the distribution of pixel values on a constant hue plane in the RGB color space. The constant hue plane is represented as the triangle whose vertices correspond to white, black, and the maximally saturated colors. By spreading out the distribution of the pixel values on the constant hue plane, a remedy for the gamut problem and hue-preserving contrast enhancement are realized. Through experiments, the effectiveness of the proposed method is verified.
关键词： gamut problem,Contrast enhancement,hue-preserving transformation,histogram specification
更新于2025-09-10 09:29:36
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Quenched Stochastic Optical Reconstruction Microscopy (qSTORM) with Graphene Oxide
摘要： Quenched Stochastic Optical Reconstruction Microscopy (qSTORM) was demonstrated with graphene oxide sheets, peptides and bacteria; a method of contrast enhancement with super-resolution fluorescence microscopy. Individual sheets of graphene oxide (GO) were imaged with a resolution of 16 nm using the quenching of fluorescence emission by GO via its large Resonant Energy Transfer (RET) efficiency. The method was then extended to image self-assembled peptide aggregates (resolution 19 nm) and live bacterial cells (resolution 55 nm, the capsular structure of E. coli from urinary tract infections) with extremely low backgrounds and high contrasts (between one and two orders of magnitude contrast factor improvements that depended on the thickness of the graphene oxide layer used). Graphene oxide films combined with STORM imaging thus provide an extremely convenient method to image samples with large backgrounds due to non-specifically bound fluorophores (either due to excess labelling or autofluorescent molecules), which is a common occurrence in studies of both biological cells and soft-condensed matter. The GO quenches the fluorescence across a thin layer at distances of less than 15 nm. Graphene oxide films coated with thin layers (≤15 nm) of polystyrene, polymethylmethacrylate and polylysine are shown to be effective in producing high contrast qSTORM images, providing a convenient modulation of sample/substrate interactions. The GO coatings can also provide an increased image resolution and a factor of 2.3 improvement was observed with the peptide fibres using a feature of interest metric,when there was a large non-specifically bound background.
关键词： Quenched Stochastic Optical Reconstruction Microscopy,contrast enhancement,peptide aggregates,qSTORM,fluorescence quenching,RET,bacterial cells,Resonant Energy Transfer,graphene oxide,super-resolution fluorescence microscopy
更新于2025-09-10 09:29:36
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[IEEE 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) - Atlanta, GA (2017.10.21-2017.10.28)] 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) - Comparing Different Preprocessing Methods in Automated Segmentation of Retinal Vasculature
摘要： Computer methods and image processing provide medical doctors assistance at any time and relieve their work load, especially for iterative processes like identifying objects of interest such as lesions and anatomical structures from the image. Vescular detection is considered to be a crucial step in some retinal image analysis algorithms to find other retinal landmarks and lesions, and their corresponding diameters, to use as a length reference to measure objects in the retina. The objective of this study is to compare effect of two preprocessing methods on retinal vessel segmentation methods, Laplacian-of-Gaussian edge detector (using second-order spatial differentiation), Canny edge detector (estimating the gradient intensity), and Matched filter edge detector either in the normal fundus images or in the presence of retinal lesions like diabetic retinopathy. The steps for the segmentation are as following: 1) Smoothing: suppress as much noise as possible, without destroying the true edges, 2) Enhancement: apply a filter to enhance the quality of the edges in the image (sharpening), 3) Detection: determine which edge pixels should be discarded as noise and which should be retained by thresholding the edge strength and edge size, 4) Localization: determine the exact location of an edge by edge thinning or linking. From the accuracy view point, comparing to manual segmentation performed by ophthalmologists for retinal images belonging to a test set of 120 images, by using first preprocessing method, Illumination equalization, and contrast enhancement , the accuracy of Canny, Laplacian-of-Gaussian, and Match filter vessel segmentation was more than 85% for all databases (MUMS-DB, DRIVE, MESSIDOR). The performance of the segmentation methods using top-hat preprocessing (the second method) was more than 80%. And lastly, using matched filter had maximum accuracy for the vessel segmentation for all preprocessing steps for all databases.
关键词： contrast Enhancement,image processing,Diabetic retinopathy,top hat transformation,Laplacian-of-Gaussian edge detector,Illumination Equalization,retinal blood vessel,Match filter,Canny edge detector
更新于2025-09-09 09:28:46
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Bio-inspired reaction diffusion system applied to image restoration
摘要： In this paper, we propose a new model of nonlinear and anisotropic reaction diffusion system applied to image restoration and to contrast enhancement. This model is based on a system of partial differential equations of type Fitzhugh-Nagumo. In the first, we give the comparison with the previous model, then, we show the robustness and the performance of our algorithm through a number of experimental results.
关键词： reaction diffusion system,image restoration,nonlinear anisotropic diffusion,Fitzhugh-Nagumo model,mathematical biology model,contrast enhancement
更新于2025-09-09 09:28:46
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Contrast Enhancement Effect on High Dynamic Range Image Registration Using Mutual Information
摘要： Mutual Information (MI) is emerging as a very strong metric for image registration purposes in the literature. It has many applications from remote sensing to medical image registration. From this wide range of use of MI, images are mostly expressed in different numbers of bits (high dynamic range) especially in medical and satellite imaging. In such cases, contrast enhancement becomes inevitable before MI-based image registration since all the images should be in the same intensity range. The change in intensities in images will directly affect MI metric. Contrast enhancement methods also have a significant effect on the registration performance due to MI metric and this problem is not sufficiently addressed in the literature. In this paper, the effect of the outstanding contrast enhancement methods is examined on image registration performance. For this purpose, high dynamic range satellite images were used and Monte Carlo tests were performed. They are tried to be aligned with MI and constrained optimization by linear approximations (COBYLA) optimization algorithm. Consequently, it is found that contrast enhancement methods have an effect on MI-based image registration. It is concluded that Laplacian of Gaussian unsharp blending masks (AHE) and (LoGUnsarp), adaptive histogram equalization (CLAHE) limited adaptive histogram equalization contrast methods have better registration performance. They can be preferred in such registration purposes.
关键词： contrast enhancement,mutual information,optimization,image registration
更新于2025-09-09 09:28:46
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An improved luminosity and contrast enhancement framework for feature preservation in color fundus images
摘要： Insufficient luminosity and poor local contrast are the major hurdles affecting the visual quality of the fundus images. A suitable framework is proposed for the enhanced visual perception of color fundus images based on a hybrid approach that combines gamma correction and singular value equalization for luminosity enhancement and contrast-limited adaptive histogram equalization (CLAHE) for local contrast enhancement. Luminosity enhancement is done by performing singular value equalization of the low-frequency component of the original value channel of the image in hue, saturation, and value color space using the low-frequency component of the gamma-corrected value channel of the same image. Discrete wavelet transform is applied for extracting the corresponding low-frequency components from the original and gamma-corrected value channels. Local contrast enhancement is achieved using CLAHE performed on the luminosity channel in L ?a?b? color space. The performance of the proposed method is analyzed qualitatively based on visual assessment and quantitatively with the parameters such as peak signal-to-noise ratio, absolute mean brightness error, discrete entropy and measure of enhancement. Experiments conducted on the color fundus images show improved results with sufficient detail preservation and enhanced visual perception compared to the existing methods.
关键词： Gamma correction,Color fundus images,Luminosity enhancement,Singular value equalization,Local contrast enhancement
更新于2025-09-04 15:30:14
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A software supported image enhancement approach based on DCT and quantile dependent enhancement with a total control on enhancement level
摘要： In many computer vision applications like medical imaging, pattern recognition etc., image enhancement is an important pre-processing requirement which is used to improve the efficiency of an application. A significantly large literature is available on image enhancement; unfortunately, most of these schemes have certain shortcomings for e.g. the lack of control over the contrast starching, noise enhancement and ‘mean-shift’ problem etc. To deal with the aforementioned problems, this study suggests an efficient method which is based on discrete cosine transformation (DCT) and quantile dependent sub-division of the histogram of given input image. In the proposed method, we apply DCT on the input image to get low-frequency component (LFC) and then use the quantile-based sub-division on the histogram of LFC. Finally, histogram equalization is performed on all these sub-histograms separately. The main advantage of quantile-based segmentation is that here entire intensity spectrum participates in the enhancement process, which provides a total control over the enhancement level. In the proposed method the high-frequency component remains untouched and hence the structural information of the input image and the noise in the input image remains unaffected by the image enhancement process.
关键词： Linearly quantile separated histogram equalization,Contrast enhancement,Mean-shift problem,Discrete cosine transform,Greyscale images
更新于2025-09-04 15:30:14
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[IEEE 2018 IEEE 7th Global Conference on Consumer Electronics (GCCE) - Nara, Japan (2018.10.9-2018.10.12)] 2018 IEEE 7th Global Conference on Consumer Electronics (GCCE) - Histogram-Based Image Pre-processing for Machine Learning
摘要： This paper proposes to use some image processing methods as a data normalization method for machine learning. Conventionally, z-score normalization is widely used for pre-processing of data. In the proposed approach, in addition to z-score normalization, a number of histogram-based image processing methods such as histogram equalization are applied to training data and test data as a pre-processing method for machine learning. We evaluate the effectiveness of the proposed approach by using a support vector machine algorithm and a random forest one. In experiments, the proposed scheme is applied to a face-based authentication algorithm with SVM/random forest classifiers to confirm the effectiveness. For SVM classifiers, both z-score normalization and image enhancement work well as a pre-processing method for improving the accuracy. In contrast, for random forest classifiers, a number of image enhancement methods work well, although z-score normalization is unuseful for improving the accuracy.
关键词： Support Vector Machines,Pre-processing,Contrast Enhancement,Random Forest,Machine Learning
更新于2025-09-04 15:30:14
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[IEEE 2018 1st International Cognitive Cities Conference (IC3) - Okinawa, Japan (2018.8.7-2018.8.9)] 2018 1st International Cognitive Cities Conference (IC3) - A Hardware-Oriented Contrast Enhancement Algorithm for Real-Time Applications
摘要： Recently, electronics, advanced driver assistance system (ADAS) and intelligent transport system (ITS) have become to make great strides towards to the goal of automatic driving and smart cities. Therefore, how to enhance the visibility no matter at night or excessive exposure turns into an important issue of the research. In this paper, a novel effective contrast enhancement and hardware-oriented algorithm is proposed. The proposed contrast enhancement algorithm includes a RGB to HSV module, a mirror expansion module, a weighted filter and a Gamma correction module. A new technique called weighted filter was used to pick up the best brightness factor for the proposed algorithm. To be suitable for VLSI implementation, the proposed algorithm was developed based on low-complexity and low-memory-requirement. Compared with previous studies, this work proposed a high quality and low complexity hardware-oriented contrast enhancement algorithm for VLSI implementation.
关键词： VLSI,weighted filter,Gamma correction,contrast enhancement,mirror expansion
更新于2025-09-04 15:30:14
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Variance Based Brightness Preserved Dynamic Histogram Equalization for Image Contrast Enhancement
摘要： This paper proposes a novel variant of Brightness Preserving Dynamic Histogram Equalization (BPDHE) having more brightness preserving capability with less computational time. This variant, called Variance based Brightness Preserve Dynamic Histogram Equalization (VBBPDHE) uses the interclass and intraclass variance information to segment out the histogram recursively. This variant does not need the smoothing operation of input histogram and also no need to compute local maxima or minima to segment out the histogram unlike BPDHE. Visual analysis, quality metrics and execution time clearly demonstrate the efficiency of the proposed VBBPDHE over well-known existing methods.
关键词： variance,color,image contrast enhancement,histogram equalization,brightness preservation
更新于2025-09-04 15:30:14