Entropy Contrast Research Articles

Exposure and Median Based One-to-One Gray Level Mapping Transformation for Entropy Preservation and Contrast Enhancement

Many histogram equalization (HE) techniques have been proposed for the contrast enhancement in the past. In recent years clipped histogram equalization techniques are developed to control the degree of over enhancement and the noise. Yet these methods are not guaranteed to preserve the gray levels and thus the information in output image is less than that in the input image, even though it has been enhanced. We propose two new one-to-one gray level mapping (OGM) transformation methods, namely exposure based one-to-one gray level mapping (EOGM) transformation and median based one-to-one gray level mapping (MOGM) transformation. In EOGM and MOGM methods histogram is divided into two sub histograms based on exposure and median of the images respectively. Weights for these sub histograms are calculated and then OGM transformation function is applied to these sub histograms by using the derived weights. This transformation addresses both over enhancement and gray level loss effectively and also ensure uniform degree of enhancement. This preserves all the information content even after enhancement with all structural details, ensures no false contouring. Thus they are suitable for medical image applications, where information loss leads to wrong diagnosis. The experimental results show the supremacy of our methods over existing HE methods.

High Interfacial Barriers at Narrow Carbon Nanotube-Water Interfaces.

Water displays anomalous fast diffusion in narrow carbon nanotubes (CNTs), a behavior that has been reproduced in both experimental and simulation studies. However, little is reported on the effect of bulk water-CNT interfaces, which is critical to exploiting the fast transport of water across narrow carbon nanotubes in actual applications. Using molecular dynamics simulations, we investigate here the effect of such interfaces on the transport of water across arm-chair CNTs of different diameters. Our results demonstrate that diffusion of water is significantly retarded in narrow CNTs due to bulk regions near the pore entrance. The slowdown of dynamics can be attributed to the presence of large energy barriers at bulk water-CNT interfaces. The presence of such intense barriers at the bulk-CNT interface arises due to the entropy contrast between the bulk and confined regions, with water molecules undergoing high translational and rotational entropy gain on entering from the bulk to the CNT interior. The intensity of such energy barriers decreases with increase in CNT diameter. These results are very important for emerging technological applications of CNTs and other nanoscale materials, such as in nanofluidics, water purification, nanofiltration, and desalination, as well as for biological transport processes.

A novel adaptive fast partition algorithm based on CU complexity analysis in HEVC

High efficiency video coding (HEVC) is the latest video coding standard. Compared with H.264, the proposal of quad-tree structure not only improves the coding efficiency greatly but also increases complexity of coding. Therefore, this paper performed a fast-adaptive algorithm based on the complexity of images for intra prediction. Two values from micro and macro levels are considered for each coding unit block. We use entropy on the macroscopic level and texture contrast on the microscopic level. Relatively, large pictures always have more smooth blocks and there are more complex blocks in small pictures. To deal with this problem, this study uses adaptive process to make the values of entropy and texture contrast close to the ideal values of the current video, thereby making the division reasonable. Experimental results show that the proposed algorithm can reduce 34.6% coding time on average, with a loss of Bjontegaard delta rate of 0.8%.

An improved algorithm based on CLAHE for ultrasonic well logging image enhancement

This study presents a novel image enhancement method in consideration of low contrast and blurred details of ultrasonic well logging images by combining the contrast limited adaptive histogram equalization (CLAHE) with power-law transformation. The red, green, and blue (RGB) color image is first transformed into hue, saturation, and intensity (HSI) space and the CLAHE is applied only to the intensity (or luminance) component (I) of the image. This enhances local details and prevents excess brightness in flat areas and uniform regions while maintaining the chromaticity values of the degraded image. Limitation of the CLAHE technique is that it limits the amplification of contrast by clipping the histogram of every sub-region at a predefined clip-limit. The power-law transformation is then applied to the processed image following the CLAHE processing. This maps a wide range of dark input values into a narrower range of output values, whereas a narrow range of bright input values into a wider range of output values, which further improve the contrast and highlight the local details. Finally, the result is presented as an RGB image. The performance of the proposed technique is compared with histogram equalization and CLAHE methods based on image quality objective measurement tools such as the mean square error (MSE) involving peak signal-to-noise ratio (PSNR), information entropy (IE), and luminance contrast (LC). Experimental results show that the proposed algorithm produces better ultrasonic well logging images than the existing enhancement schemes. The underlying improvement is of great significance for further upgrading the accuracy of sonic imaging logging data interpretation.

Swarm intelligence optimized piecewise gamma corrected histogram equalization for dark image enhancement

In this paper, a highly adaptive swarm intelligence optimized dark image enhancement approach is proposed for remotely sensed satellite images. Here, a weighted summation framework is suggested for imparting “on-demand entropy restoration and contrast enhancement”. This approach utilizes the benefits of both gamma correction and histogram equalization; and hence, overall image enhancement can be appropriately imposed without losing original image features, especially for dark satellite images. For further improvement, gamma correction is also employed in a piecewise manner, separately for dark as well as light pixel values, so that over-saturation and other related unnatural artifacts can be avoided. A suitable entropy and contrast based cost function is utilized, and its maximization is done by employing particle swarm optimization over a three-dimensional search space. The proposed approach is found to be highly appreciable for overall enhancement, preserving all the intrinsic visual details for a wide range of dark image database covering satellite as well as general images.

THE EVOLUTION OF GAS GIANT ENTROPY DURING FORMATION BY RUNAWAY ACCRETION

ABSTRACT We calculate the evolution of gas giant planets during the runaway gas accretion phase of formation, to understand how the luminosity of young giant planets depends on the accretion conditions. We construct steady-state envelope models, and run time-dependent simulations of accreting planets with the code Modules for Experiments in Stellar Astrophysics. We show that the evolution of the internal entropy depends on the contrast between the internal adiabat and the entropy of the accreted material, parametrized by the shock temperature T 0 and pressure P 0. At low temperatures ( – , depending on model parameters), the accreted material has a lower entropy than the interior. The convection zone extends to the surface and can drive a high luminosity, leading to rapid cooling and cold starts. For higher temperatures, the accreted material has a higher entropy than the interior, giving a radiative zone that stalls cooling. For , the surface–interior entropy contrast cannot be accommodated by the radiative envelope, and the accreted matter accumulates with high entropy, forming a hot start. The final state of the planet depends on the shock temperature, accretion rate, and starting entropy at the onset of runaway accretion. Cold starts with require low accretion rates and starting entropy, and the temperature of the accreting material needs to be maintained close to the nebula temperature. If instead the temperature is near the value required to radiate the accretion luminosity, , as suggested by previous work on radiative shocks in the context of star formation, gas giant planets form in a hot start with .

Medical Images Contrast Enhancement using Quad Weighted Histogram Equalization with Adaptive Gama Correction and Homomorphic Filtering

Image contrast enhancement plays a vital role in various applications of digital image processing field like face recognition, satellite imaging and medical imaging. This paper proposes an efficient algorithm to cater the limitation of over enhancement with maximum entropy preservation. In the proposed algorithm, input image histogram is segmented first based on its valley positions and then weighted distribution is applied to all segmented sub histograms followed by the histogram equalization, gamma correction and homomorphic filtering. Results reveal that the proposed technique outer performs other conventional histogram equalization techniques both in terms of visual quality along with maximum entropy preservation and contrast enhancement.

Phase adjustment for multistatic passive radar imaging based on image entropy and image contrast

ABSTRACTMultistatic passive radar imaging system (MPRIS) does not behave as well as traditional inverse synthetic aperture radar (ISAR) imaging system does on account of the limited illuminators of opportunity and relatively narrow bandwidth of the signals. Furthermore, in the MPRIS, limited by the unavoidable measurement errors of the positions of the illuminators or receivers, or the manoeuvrability of the moving target, the translation motion of the target cannot be compensated accurately, in which the echo will be contaminated by an error phase, resulting in the occurrence of serious image defocus and the degeneracy of the image quality. In this article, two nonparametric autofocusing techniques based on the principles of minimum entropy and maximum contrast, respectively, are proposed to deal with the undesired problems under the multistate geographical configuration consisting of one illuminator and several receivers distributed around a target. The error phase is corrected by introducing an adjustment phase in the frequency domain and the adjustment phase can be searched by iteratively solving an equation that is derived by optimizing the quality measure of the image. Since no assumption is made during the derivation, the proposed techniques could be used to compensate any form of the phase error. In addition, the influence of the multistatic configuration on the image resolution is analysed and an optimized configuration is obtained. The effectiveness of the proposed algorithms is verified via the theoretical derivations and simulations.

A Naïve Visual Cryptographic Algorithm for the Transfer of a Compressed Medical Images

The transmission of a suitably compressed image over a bandwidth, over long distances, gives rise towards a new era in the field of information technology. A gradual increase in this appending scenic application, involving the transfer of the images securely over the Ethernet has become an increasingly important aspect to be addressed during thou phenomenon, especially in the transfer of the digital medical images vividly, encapsulated with abundant information related to these images. The compressed medical images of the DICOM format contain certain amount of confidential data, pertaining to a clinical research or to an individual, and the confidentiality of the same has to be preserved from various security threats and eves-dropping. With a widespread applications among various multimedia applicative systems, telemedicine, medical imaging, military and certain safety-critical applications, inter-net and intra-net communicative applications, etc, a reliable transfer of suitable information, efficiently & securely is considered as one of the revolutionary aims in today’s communication technology and visual cryptographic methodologies. Real-time applications as such detailed above majorly is concerned with the security measures and many algorithms have been developed as a proof for various visual cryptographic methodologies.
 In this paper we propose an efficient and a reliable visual cryptographic methodology which focuses on the encryption and decryption of the two-dimensional DICOM standard compressed medical image, effectively.
 This paper discusses an efficient design of 192 bit encoder using AES Rijndael Algorithm with the decomposition of an image into square image size blocks and the image blocks are shuffled using 2D CAT map.
 The shuffling of the image blocks/pixels employs a Logistic map of these image pixels coupled with 2D mapping of the pixels of the DICOM standard medical image, generated randomly, being the control parameter thereby creating a confusion between the cipher and the plain image, gradually increasing the resistive factor against the significant attacks. This paper proposes various analytical metrics such as correlation analysis, 
 entropy analysis, homogeneity analysis, energy analysis, contrast and mean of absolute deviation analysis, to evaluate the proposed algorithm, and their suitability in image encryption applications.

Convective settling in main sequence stars: Li and Be depletion

The process of convective settling is based on the assumption that a small fraction of the low-entropy downflows sink from the photosphere down to the bottom of the star's envelope convection zone retaining a substantial entropy contrast. We have previously shown that this process could explain the slow Li depletion observed in the Sun. We construct a parametric model of convective settling to investigate the dependence of Li and Be depletion on stellar mass and age. Our model is generally in good agreement with the Li abundances measured in open clusters and solar twins, although it seems to underestimate the Li depletion in the first ~1 Gyr. The model is also compatible with the Be abundances measured in a sample of field stars.

Brain Entropy Mapping Using fMRI

Entropy is an important trait for life as well as the human brain. Characterizing brain entropy (BEN) may provide an informative tool to assess brain states and brain functions. Yet little is known about the distribution and regional organization of BEN in normal brain. The purpose of this study was to examine the whole brain entropy patterns using a large cohort of normal subjects. A series of experiments were first performed to validate an approximate entropy measure regarding its sensitivity, specificity, and reliability using synthetic data and fMRI data. Resting state fMRI data from a large cohort of normal subjects (n = 1049) from multi-sites were then used to derive a 3-dimensional BEN map, showing a sharp low-high entropy contrast between the neocortex and the rest of brain. The spatial heterogeneity of resting BEN was further studied using a data-driven clustering method, and the entire brain was found to be organized into 7 hierarchical regional BEN networks that are consistent with known structural and functional brain parcellations. These findings suggest BEN mapping as a physiologically and functionally meaningful measure for studying brain functions.

Investigation of self-adaptive LED surgical lighting based on entropy contrast enhancing method

Investigation was performed to explore the possibility of enhancing contrast by varying the spectral distribution (SPD) of the surgical lighting. The illumination scenes with different SPDs were generated by the combination of a self-adaptive white light optimization method and the LED ceiling system, the images of biological sample are taken by a CCD camera and then processed by an ‘Entropy’ based contrast evaluation model which is proposed specific for surgery occasion. Compared with the neutral white LED based and traditional algorithm based image enhancing methods, the illumination based enhancing method turns out a better performance in contrast enhancing and improves the average contrast value about 9% and 6%, respectively. This low cost method is simple, practicable, and thus may provide an alternative solution for the expensive visual facility medical instruments.

Orientation Contrast and Entropy Contrast in the Genesis of Subjective Contours along Thin Lines

Subjective contours are widely considered to be an aspect of the perception of occlusion, but considerations of occlusion do not always drive predictions of their strength. Occluding surfaces have no necessary relationship to the contours they occlude, yet it is commonly predicted that subjective contours will be strongest for inducer alignments that are orthogonal to inducer orientations. In several papers we have proposed that a lack of relationship between inducers and their alignment promotes seeing subjective contours. We explore this further here using horizontal or near-horizontal thin-line inducers arranged vertically with linearly aligned terminations along central gaps. Subjective contour strength was measured using the method of paired comparison in two experiments. The weakest subjective contours were found when the gap was orthogonal to the inducers and parallel to the outer edges of the line set. Subjective contours were strengthened by orientation contrast, defined either as a nonorthogonal relationship between the gap and the inducers or as nonparallelism between the gap and the outer alignments of the inducers. The effect was replicated at both high and low line densities. We also confirmed a strong effect of high inducer entropy (variations in inducer orientation and separation) with thin-line inducers. The results support the view that the lack of a relationship of alignments to what is aligned is a major determinant of subjective contour strength.

Overshooting by convective settling

We study a process of slow mixing in stars with convective envelopes, which is driven by the settling of cool downward plumes below the base of the convection zone. If a small fraction (of order $10^{-7}$) of the material cooled at the surface retains a significant entropy deficit while descending in plumes, it can reach the depth where lithium burning takes place. The model calculates the thermal response and mixing below the convection zone due to the settling process, assuming that the plumes arrive at the base of the convection zone with a broad range of entropy contrasts. We obtain a good fit to the observed lithium depletion in the Sun by assuming that the settling mass flux is distributed with respect to the entropy contrast as a power law with a slope around -2. We find convective settling to have a negligible influence on the stratification below the convection zone, although mixing induced by it could modify the gradient of helium concentration.

Generalized Majority Logic Criterion to Analyze the Statistical Strength of S-Boxes

The majority logic criterion is applicable in the evaluation process of substitution boxes used in the advanced encryption standard (AES). The performance of modified or advanced substitution boxes is predicted by processing the results of statistical analysis by the majority logic criteria. In this paper, we use the majority logic criteria to analyze some popular and prevailing substitution boxes used in encryption processes. In particular, the majority logic criterion is applied to AES, affine power affine (APA), Gray, Lui J, residue prime, S8 AES, Skipjack, and Xyi substitution boxes. The majority logic criterion is further extended into a generalized majority logic criterion which has a broader spectrum of analyzing the effectiveness of substitution boxes in image encryption applications. The integral components of the statistical analyses used for the generalized majority logic criterion are derived from results of entropy analysis, contrast analysis, correlation analysis, homogeneity analysis, energy analysis, and mean of absolute deviation (MAD) analysis.

Compounded direct pixel beamforming for medical ultrasound imaging

In this paper, a new compounded direct pixel beamforming (CDPB) method is presented to remove blurring artifacts introduced by ultrasound scan conversion. In CDPB, receive focusing is directly performed on each display pixel in Cartesian coordinates using the raw RF data from adjacent transmit firings so that artifacts from the scan conversion can be removed. In addition, the energy variations resulting from the distance between the transmit scanline and display pixel are compensated by utilizing the gain factor obtained from the ultrasound beam pattern. The proposed CDPB method was evaluated using simulation and in vivo liver data acquired by a commercial ultrasound machine equipped with a research package. The experimental results showed that the proposed CDPB method improved the information entropy contrast (IEC) by 23.6% compared with the conventional scan conversion method and it reduced the blocking artifacts factor (BAF) by 16.4% over the direct pixel-based focusing method. These results indicate the proposed new direct pixel beamforming method could be used to enhance image quality in medical ultrasound imaging.

Stereo Image Quality Assessment Using Visual Attention and Distortion Predictors

Several metrics have been reported in the literature to assess stereo image quality, mostly based on visual attention or human visual sensitivity based distortion prediction with the help of disparity information, which do not consider the combined aspects of human visual processing. In this paper, visual attention and depth assisted stereo image quality assessment model (VAD-SIQAM) is devised that consists of three main components, i.e., stereo attention predictor (SAP), depth variation (DV), and stereo distortion predictor (SDP). Visual attention is modeled based on entropy and inverse contrast to detect regions or objects of interest/attention. Depth variation is fused into the attention probability to account for the amount of changed depth in distorted stereo images. Finally, the stereo distortion predictor is designed by integrating distortion probability, which is based on low-level human visual system (HVS), responses into actual attention probabilities. The results show that regions of attention are detected among the visually significant distortions in the stereo image pair. Drawbacks of human visual sensitivity based picture quality metrics are alleviated by integrating visual attention and depth information. We also show that positive correlation with ground-truth attention and depth maps are increased by up to 0.949 and 0.936 in terms of the Pearson and the Spearman correlation coefficients, respectively.

Conceptual thresholds for same and different in old-(Macaca mulatta) and new-world (Cebus apella) monkeys

Learning of the relational same/different (S/D) concept has been demonstrated to be largely dependent upon stimulus sets containing more than two items for pigeons and old-world monkeys. Stimulus arrays containing several images for use in same/different discrimination procures (e.g. 16 identical images vs. 16 nonidentical images) have been shown to facilitate and even be necessary for learning of relational concepts (Flemming et al., 2007; Wasserman et al., 2001; Young et al., 1997). In the present study, we investigate the threshold at which a new world primate, the capuchin (Cebus apella) may be able to make such a discrimination. Utilizing a method of increasing entropy, rather than conventional procedures of decreasing entropy, we demonstrate unique evidence that capuchin monkeys are readily capable of making 2-item relational S/D conditional discriminations. In another experiment, we examine the supposed level of difficulty in making S/D discriminations by rhesus monkeys (Macaca mulatta). Whereas pigeons (Columba livia) and baboons (Papio papio) have shown marked difficulty simultaneously discriminating same from different arrays at all when composed of fewer than 8 items each, rhesus monkeys seem to understand that pairs of stimuli connote sameness and difference just the same (Flemming et al., 2007). With sustained accurate performance of 2-item S/D discriminations, both experienced and task-naïve rhesus monkeys appear quite certain in their conceptual knowledge of same and different. We conclude that learning of the same/different relational concept may be less dependent upon high levels of entropy contrast than originally hypothesized for nonhuman primates.

Salient Region Detection by Modeling Distributions of Color and Orientation

We present a robust salient region detection framework based on the color and orientation distribution in images. The proposed framework consists of a color saliency framework and an orientation saliency framework. The color saliency framework detects salient regions based on the spatial distribution of the component colors in the image space and their remoteness in the color space. The dominant hues in the image are used to initialize an expectation-maximization (EM) algorithm to fit a Gaussian mixture model in the hue-saturation (H-S) space. The mixture of Gaussians framework in H-S space is used to compute the inter-cluster distance in the H-S domain as well as the relative spread among the corresponding colors in the spatial domain. Orientation saliency framework detects salient regions in images based on the global and local behavior of different orientations in the image. The oriented spectral information from the Fourier transform of the local patches in the image is used to obtain the local orientation histogram of the image. Salient regions are further detected by identifying spatially confined orientations and with the local patches that possess high orientation entropy contrast. The final saliency map is selected as either color saliency map or orientation saliency map by automatically identifying which of the maps leads to the correct identification of the salient region. The experiments are carried out on a large image database annotated with ldquoground-truthrdquo salient regions, provided by Microsoft Research Asia, which enables us to conduct robust objective level comparisons with other salient region detection algorithms.

Image-fusion-based active contour model

We propose a novel B-spline active contour model based on image fusion. Compared with conventional active contours, this active contour has two advantages. First, it is represented by a cubic B-spline curve, which can adaptively determine the curve parameter’s step length; and it can also effectively detect and express the object contour’s corner points. Second, it is implemented in connection with image fusion. Its external image force is modified as the weighted sum of two modal image forces, with the two weights in terms of a local region’s image entropy or image contrast’s standard deviation. The experiments indicate that this active contour can accurately detect both the object’s contour edge and the corner points. Our experiments also indicate that the active contour’s convergence with a weighted image force by the image contrast’s standard deviation is more accurate than that of image entropy, restraining the influence of the texture or pattern.