Contrast For Image Research Articles

A novel contrast and saturation prior for image dehazing

A novel contrast and saturation prior for image dehazing

Calculation of contrast for computer simulated resolution chart image

The work is devoted to the analysis of the resolution chart image created with the Image Simulation mode of ZEMAX software. The changes in image quality are simulated using the model of Helios-44M-4 photographic objective lens. The research is conducted for monochromatic light conditions and computer simulated resolution test chart as an object for the lens with different chart positions. An axial beam and an off-axis beam that corresponds to the maximum object space field angle are analyzed. The calculation of contrast of the simulated resolution chart images is made modeling different spatial frequencies of the chart lines. The contrast is calculated in three ways. The first way uses the maximum and minimum values of the image illumination, and the second and third ways are based on the integral values of the illumination of black and white lines, calculated with the method of rectangles and the method of trapeziums, respectively. Having been calculated the values of contrast are compared with the theoretical values of the diffraction modulation transfer function obtained with ZEMAX.

Calculation of effective atomic numbers using a rational polynomial approximation method with a dual-energy X-ray imaging system.

The study aims to develop a rational polynomial approximation method for improving the accuracy of the effective atomic number calculation with a dual-energy X-ray imaging system. This method is based on a multi-materials calibration model with iterative optimization, which can improve the calculation accuracy of the effective atomic number by adding a rational term without increasing the computation time. The performance of the proposed rational polynomial approximation method is demonstrated and validated by both simulated and experimental studies. The twelve reference materials are used to establish the effective atomic number calibration model, and the value of the effective atomic numbers are between 5.444 and 22. For the accuracy of the effective atomic number calculation, the relative differences between calculated and experimental values are less than 8.5%for all sample cases in this study. The average calculation accuracy of the method proposed in this study can be improved by about 40%compared with the conventional polynomial approximation method. Additionally, experimental quality assurance phantom imaging result indicates that the proposed method is compliant with the international baggage inspection standards for detecting the explosives. Moreover, the experimental imaging results reveal that the difference of color between explosives and the surrounding materials is in significant contrast for the dual-energy image with the proposed method.

Lab Image and Field Image Contrast Quality Differentiation

Image processing performance can be improved with the process of resizing the original input image to one standard size. Most of the previous studies used a standard size of 256 x 256 to provide the image as the image pre-processing material. The result of different image size dimension are shows in this research to proven that image resizing is important. Reducing image dimension size can help to improve system performance. At the same time, it is importance to keep the image quality. This study shows that by reducing image dimension, it can improve the computer or system performance more than 95%. Image quality can be measured to get helpful information for the study after resizing the image into the same standard size. In this study, measurement of contrast levels was taken to compare the quality differences between image labs and field images. It turns out that the quality of lab image produces high-quality images with good brightness over image field image.The best quality image is the images that have low contrast. Therefore in this research paper we used CLAHE method to enhance the contrast and brightness for field image.

NO-REFERENCE QUANTITATIVE ASSESSMENT OF GENERALIZED CONTRAST FOR COMPLEX IMAGES

Context. Nowadays the task of automatically measuring of image quality in real time is extremely relevant for the vast majority of practical applications. No-reference quantitative assessment of image quality is one of the most pressing and difficult problems of image processing. Generalized contrast is the most important quantitative characteristic which determines the objective quality of the image. Currently, the development of new effective methods of no-reference measuring of generalized contrast for complex image in automatic mode which have the level of computing costs, which are acceptable to implement the processing in real time, is one of the most urgent tasks of image pre-processing.Objective. Development of new histogram-based method for no-reference measurement of generalized contrast of complex images on the basis of the mean value for all contrast values of all pairs of image elements (objects and background) for various definitions of contrast kernel.Method. Analysis of known approaches to measurement of a local contrast of the image elements, of known methods of the quantitative assessment of generalized contrast of complex images as well of the results of experimental research for a series of complex real and test images allowed to reveal inherent patterns (accordance to basic requirements to the definition of contrast, the nature and the dynamic of contrast changes at the linear transformations of the brightness scale), which are manifested depending on the use of the different definitions of the contrast kernels and the metrics of generalized contrast of images.Results. New histogram-based method for no-reference measurement of generalized contrast for complex images is proposed. No-reference contrast metrics for the histogram-based measuring of generalized contrast of complex images on the basis of the average contrast of image elements for different definitions of contrast kernel is proposed.Conclusions. Proposed no-reference metrics on the basis of the average contrast of image elements for proposed contrast kernels allow providing accurate quantitative assessment of generalized contrast of the real complex images and enable to evaluate (predict) with reasonable accuracy the perceived image quality at carrying out of subjective (qualitative) expert estimates.

HISTOGRAM-BASED METHOD FOR NO-REFERENCE MEASUREMENT OF GENERALIZED CONTRAST OF COMPLEX IMAGES

Background. Nowadays the task of automatically measuring of image quality in real time is extremely relevant for the vast majority of practical applications. No-reference quantitative assessment of image quality is one of the most pressing and difficult problems of image processing. Generalized contrast is the most important quantitative characteristic which determines the objective quality of the image. Currently, the development of new effective methods of no-reference measuring of generalized contrast for complex image in automatic mode with the level of computing costs, which are acceptable to implement the processing in real time, is one of the most urgent tasks of image preprocessing. Objective. Development of new histogram-based method for no-reference measurement of generalized contrast of complex (multi-element) images based on the average contrast of image elements (objects and background) for different definitions of contrast kernel. Methods. Analysis of known approaches to measurement of a local contrast of the image elements, of known methods of the quantitative assessment of generalized contrast of complex images as well as of the experimental research results for a series of complex real and test images allowed revealing inherent patterns (accordance to basic requirements to the definition of contrast, the nature and the dynamic of contrast changes at the linear transformations of the brightness scale), which are manifested depending on the use of the different definitions of the contrast kernels and the metrics of generalized contrast of images. Results. No-reference contrast metrics for the histogram-based measuring of generalized contrast of complex images based on the average contrast of image elements for different definitions of contrast kernel is proposed. Conclusions. Proposed no-reference metrics based on the average contrast of image elements for proposed contrast kernels allow providing accurate quantitative assessment (measurement) of generalized contrast of the real complex images and enable to evaluate (predict) with reasonable accuracy the perceived image quality at carrying out of subjective (qualitative) expert estimates.

Separation of Granularity into Uniformity Deterioration Factors for Electrophotographic Images

It is important to not only evaluate granularity but also clarify the factors affecting it in order to improve the graininess of an image. A method for separating graininess into elements on the basis of the fluctuation of the background, lightness, and toner adherent area is proposed. By using this method following estimations will be enabled; 1. By obtaining the relation between the ordinary graininess and elements, the effect of each elements to the image quality can be estimated. This makes it possible to categorize printer. 2. Measuring by means of a polarizing plate, an effect of gloss ununiformity to the image quality can be detected. 3. By defining the standard contrast for a monochrome image, even though the dot density differs, the area fluctuation factor of graininess can be compared.

Bayesian reconstruction of multiscale local contrast images from brain activity

BackgroundRecent advances in functional magnetic resonance imaging (fMRI) techniques make it possible to reconstruct contrast-defined visual images from brain activity. In this manner, the stimulus images are represented as the weighted sum of a set of element images with different scales. The contrast weight of local images were decoded using fMRI activity recorded when the subject was viewing the stimulus images. Multivariate methods, such as the sparse multinomial logistic regression model (SMLR), have been proven effective for learning the mapping between fMRI patterns of primary visual cortex voxels and contrast of stimulus images. However, the SMLR method is highly time-consuming in practical application. New methodThe Naive Bayesian classifier based on independent component analysis (NB-ICA) is proposed to efficiently decode the contrast of multi-scale local images. First, temporal independent components of fMRI data which were treated as new features for NB classifier were acquired by ICA decomposition. Second, the contrast for each local element image was computed based on NB estimation theory. ResultsNB-ICA method can be used to reconstruct novel visual images. The average spatial correlation between the represented and reconstructed images was 0.41±0.13 (p<0.001). Comparison with existing method(s)At the expense of reconstruction accuracy, NB-ICA is more efficient than SMLR which reduces the computation time from hours to seconds. ConclusionsA new method, termed NB-ICA, is proposed and can efficiently reconstruct contrast-defined visual images from fMRI data. This study provides theoretical support for brain-computer interface research and also provides ideas for the study of real-time fMRI data.

Contrast enhancement and robustness improvement of adaptive ultrasound imaging using forward-backward minimum variance beamforming

In adaptive ultrasound imaging, accurate estimation of the array covariance matrix is of great importance, and biases the performance of the adaptive beamformer. The more accurately the covariance matrix can be estimated, the better the resolution and contrast can be achieved in the ultrasound image. To this end, in this paper, we have used the forward-backward spatial averaging for array covariance matrix estimation, which is then employed in minimum variance (MV) weights calculation. The performance of the proposed forward-backward MV (FBMV) beamformer is tested on simulated data obtained using Field II. Data for two closely located point targets surrounded by speckle pattern are simulated showing the higher amplitude resolution of the FBMV beamformer in comparison to the forward-only (F-only) MV beamformers, without the need for diagonal loading. A circular cyst with a diameter of 6 mm and a phantom containing wire targets and two cysts with different diameters of 8 mm and 6 mm are also simulated. The simulations show that the FBMV beamformer, in contrast to the F-only MV, could estimate the background speckle statistics without the need for temporal smoothing, resulting in higher contrast for the FBMV-resulted image in comparison to the MV images. In addition, the effect of steering vector errors is investigated by applying an error of the sound speed estimate to the ultrasound data. The simulations show that the proposed FBMV beamformer presents a satisfactory robustness against data misalignment resulted from steering vector errors, outperforming the regularized F-only MV beamformer. These improvements are achieved without compromising the good resolution of the MV beamformer and resulted from more accurate estimation of the covariance matrix and consequently, the more accurate setting of the MV weights.

The contrast and illumination technique for image capturing under different Tungsten illumination

The digital camera which contain light unit inside it is useful with low illumination but not for high. For different intensity; the quality of the image will not stay good but it will have dark or low intensity so we can not change the contrast and the intensity in order to increase the losses information in the bright and the dark regions. . In this search we study the regular illumination on the images using the tungsten light by changing the intensities. The result appears that the tungsten light gives nearly far intensity for the three color bands(RGB) and the illuminated band(L).the result depend on the statistical properties which represented by the voltage ,power and intensities and the effect of this parameter on the digital camera .

The contrast and illumination technique for image capturing under different Tungsten illumination

The digital camera which contain light unit inside it is useful with low illumination but not for high. For different intensity; the quality of the image will not stay good but it will have dark or low intensity so we can not change the contrast and the intensity in order to increase the losses information in the bright and the dark regions. . In this search we study the regular illumination on the images using the tungsten light by changing the intensities. The result appears that the tungsten light gives nearly far intensity for the three color bands(RGB) and the illuminated band(L).the result depend on the statistical properties which represented by the voltage ,power and intensities and the effect of this parameter on the digital camera .

Contrast enhancement for image by WNN and GA combining PSNR with information entropy

A new contrast enhancement algorithm for image is proposed combining genetic algorithm (GA) with wavelet neural network (WNN). In-complete Beta transform (IBT) is used to obtain non-linear gray transform curve so as to enhance global contrast for an image. GA determines optimal gray transform parameters. In order to avoid the expensive time for traditional contrast enhancement algorithms, which search optimal gray transform parameters in the whole parameters space, based on gray distribution of an image, a classification criterion is proposed. Contrast type for original image is determined by the new criterion. Parameters space is, respectively, determined according to different contrast types, which greatly shrink parameters space. Thus searching direction of GA is guided by the new parameter space. Considering the drawback of traditional histogram equalization that it reduces the information and enlarges noise and background blur in the processed image, a synthetic objective function is used as fitness function of GA combining peak signal-noise-ratio (PSNR) and information entropy. In order to calculate IBT in the whole image, WNN is used to approximate the IBT. In order to enhance the local contrast for image, discrete stationary wavelet transform (DSWT) is used to enhance detail in an image. Having implemented DSWT to an image, detail is enhanced by a non-linear operator in three high frequency sub-bands. The coefficients in the low frequency sub-bands are set as zero. Final enhanced image is obtained by adding the global enhanced image with the local enhanced image. Experimental results show that the new algorithm is able to well enhance the global and local contrast for image while keeping the noise and background blur from being greatly enlarged.

A Comparative Analysis of Associative Properties of Fourier versus Walsh Digital Holograms

A digital implementation of the Fourier holography scheme with identical reference and signal beams is considered. Investigations of the properties of this scheme were reported on in a number of papers. At the same time a concept of virtual optics is widely used in data encryption and data processing, whereas digital implementation of the Fourier holography scheme may be provided using not only discrete Fourier transform. Another discrete digital transform may be used as soon as it has several properties similar to the Fourier transform. The Walsh transform is the subject to consider in this work. A comparison of digital Fourier and Walsh versions of the named holographic scheme is conducted by means of their associative properties. Considered is the retrieving of information stored in the hologram using parts of different amounts of the original data. Comparison is provided with the two types of data stored: a raster image representing visual information and an image representing a set of data bits. Comparable parameters are the mean image contrast for visual data image and the bit detection accuracy for a set of data bits.