Local Contrast Enhancement Research Articles

Sensitivity of Digital Rock Method for Pore-Space Estimation to Heterogeneity in Carbonate Formations

Summary With advancements in technology and computational capacity, the method of digital rock physics (DRP) for characterizing the storage and flow properties of a reservoir is gradually taking up the space that was once dominated by conventional methods such as routine core analysis (RCA) and special core analysis (SCAL). Unlike RCA and SCAL, the DRP method provides a nondestructive approach to deal with the core samples, which in a way is also more repeatable, economic, and is a clear improvement over the existing conventional methods in terms of its flexibility to experience the multiphysics of the rock and fluids. However, DRP still has some lacunae because the available algorithms have limitations in handling various challenges of complex lithology, such as grain/pore-boundary transition, soft/hard-matter transition, and imprints of intensity gradients of a 3D structure on 2D slices. Therefore, in this paper, we proposed a new approach to handle multiple issues by optimizing the segmentation algorithms and putting them together to standardize workflows (WFs) for reliable determination of the pore volume (PV), which could be verified with the field observation of porosity obtained using industry-standard laboratory methods and well logs. More emphasis was placed on the adaptability of the WF to deal with varying heterogeneity in the rocks. In this work, we proposed five WFs and compared them with the standard algorithms (including edge detections, watershed, and global thresholding) in terms of accuracy and computation time for a set of four homogeneous and four heterogeneous samples. We found that WF3 was the one that consistently performed better than all other WFs and some of the popular algorithms when compared one to one. We used a data-conditioning filter, contrast-limited adaptive histogram equalization (CLAHE)—a practice used in medical imagery—for local contrast enhancement in the heterogeneous carbonates to increase the signal/noise ratio of the rock-sample images. It successfully handled the contrast variability caused by the pockets of low illumination in the heterogeneous samples. Its limitation has also been detailed in the paper.

Nonlinear Exposure Intensity Based Modification Histogram Equalization for Non-Uniform Illumination Image Enhancement

Non-uniform illumination image is often generated owing to various factors, such as an improper setting in the image acquisition device and absorption or reflectance of the objects that results in the existence of different exposure regions in the image. Although Histogram Equalization (HE) is well known and widely used in image enhancement, existing HE-based methods often generate washed-out effects and show unnatural appearance due to the over-enhancement phenomenon, which limits the capabilities of achieving illumination uniformity of an image. Therefore, this study proposes a modified HE method for non-uniform illumination image, namely Nonlinear Exposure Intensity-Based Modification Histogram Equalization (NEIMHE). The proposed NEIMHE method divides the non-uniform illumination image into five sub-regions and modifies the histogram of each sub-region by setting a nonlinear weight into their cumulative density function (CDF) of histogram in each sub-region. Each modified histogram is then equalized using modified HE equations that provide the intensity expansion and different intensity mapping directions for under-exposed and over-exposed sub-regions. A total of 354 non-uniform illuminated sample images were used to evaluate the performance of the proposed NEIMHE method, qualitatively and quantitatively. The proposed NEIMHE method was compared qualitatively with five state-of-the-art methods: Backlit, Adaptive Fuzzy Exposure Local Contrast Enhancement (AFELCE), Visual Contrast Enhancement Algorithm Based on Histogram Equalization (VCEA), Exposure Region-based Multi Histogram Equalization (ERMHE); and Exposure based Sub-Image Histogram Equalization (ESIHE). The proposed NEIMHE method produced an enhanced image with more uniform illumination, better preservation of image details, and high capability of maintaining image naturalness. Quantitatively, the proposed NEIMHE method achieved the highest scores in Discrete Entropy (DE), Measure of Enhancement (EME), Measure of Enhancement by Entropy (EMEE), and Peak Signal to Noise Ratio (PSNR); it attained second-best in Absolute Mean Brightness Error (AMBE) and Lightness Order Error (LOE). From both analyses, the proposed NEIMHE method has shown its capability of enhancing different exposure regions that exist in non-uniform illumination images.

Enhanced Canny edge detection for Covid-19 and pneumonia X-Ray images

In image processing, one of the most fundamental technique is edge detection. It is a process to detect edges from images by identifying discontinuities in brightness. In this research, we present an enhanced Canny edge detection technique. This method integrates local morphological contrast enhancement and Canny edge detection. Furthermore, the proposed edge detection technique was also applied for pneumonia and COVID-19 detection in digital x-ray images by utilising convolutional neural networks. Results show that this enhanced Canny edge detection technique is better than the traditional Canny technique. Also, we were able to produce classifiers that can classify edge x-ray images into COVID-19, normal, and pneumonia classes with high accuracy, sensitivity, and specificity.

Anti-MAdCAM-1-Conjugated Nanocarriers Delivering Quantum Dots Enable Specific Imaging of Inflammatory Bowel Disease.

PurposeAssessment of inflammatory bowel disease (IBD) currently relies on aspecific clinical signs of bowel inflammation. Specific imaging of the diseased bowel regions is still lacking. Here, we investigate mucosal addressin cell adhesion molecule 1 (MAdCAM-1) as a reliable and specific endothelial target for engineered nanoparticles delivering imaging agents to obtain an exact mapping of diseased bowel foci.Materials and MethodsWe generated a nanodevice composed of PLGA-PEG coupled with anti-MAdCAM-1 antibody half-chains and loaded with quantum dots (P@QD-MdC NPs). Bowel localization and systemic biodistribution of the nanoconjugate were analyzed upon injection in a murine model of chronic IBD obtained through repeated administration of dextran sulfate sodium salt. Specificity for diseased bowel regions was also assessed ex vivo in human specimens from patients with IBD. Potential for development as contrast agent in magnetic resonance imaging was assessed by preliminary study on animal model.ResultsSynthesized nanoparticles revealed good stability and monodispersity. Molecular targeting properties were analyzed in vitro in a cell culture model. Upon intravenous injection, P@QD-MdC NPs were localized in the bowel of colitic mice, with enhanced accumulation at 24 h post-injection compared to untargeted nanoparticles (p<0.05). Nanoparticles injection did not induce histologic lesions in non-target organs. Ex vivo exposure of human bowel specimens to P@QD-MdC NPs revealed specific recognition of the diseased regions vs uninvolved tracts (p<0.0001). After loading with appropriate contrast agent, the nanoparticles enabled localized contrast enhancement of bowel mucosa in the rectum of treated mice.ConclusionP@QD-MdC NPs efficiently detected bowel inflammation foci, accurately following the expression pattern of MAdCAM-1. Fine-tuning of this nanoconjugate with appropriate imaging agents offers a promising non-invasive tool for specific IBD diagnosis.

A self-adaptive single underwater image restoration algorithm for improving graphic quality

A high-quality underwater image is essential to many industrial and academic applications in the field of image processing and analysis. Unfortunately, underwater images frequently demonstrate poor visual quality of low contrast, blurring, darkness, and color diminishing. This paper develops a new underwater image restoration framework that consists of four major phases: color correction, local contrast enhancement, haze diminution, and global contrast enhancement. A self-adaptive mechanism is designed to guide the image to either processing route based on a red deficiency measure. In the color correction phase, the histogram in each RGB channel is transformed for balancing the image color. An adaptive histogram equalization method is exploited to enhance the local contrast in the CIE-Lab color space. The dark channel prior haze removal scheme is modified for dehazing in the haze diminution phase. Finally, a histogram stretching method is applied in the HSI color space to make the image more natural. A wide variety of underwater images with various scenarios were employed to evaluate this new restoration algorithm. Experimental results demonstrated the effectiveness of our image restoration scheme as compared with state-of-the-art methods. It was suggested that our framework dramatically eliminated the haze and improved visual interpretation of underwater images.

Target recognition method of green pepper harvesting robot based on manifold ranking

In order to solve the problem of the recognition of green pepper harvesting robot in the near color background, a target recognition method based on manifold ranking is proposed in this paper. After pepper image enhancement by the local contrast enhancement algorithm, super-pixels extracted via energy-driven sampling (SEEDS) is used to construct super-pixel blocks of the enhanced image, the upper, lower, left and right boundary query nodes are used to query the boundary of the super pixel block image respectively. Then the manifold ranking is used to sort the image boundaries to obtain four saliency maps, and the final saliency map is obtained by fusion. Finally, the noise is removed by morphological operations and the contour of green pepper target is obtained to realize the recognition of green pepper in complex environment. Compared with classification and regression tree (CART), conditional random fields (CRF) and threshold, the proposed method can effectively identify the green pepper target, with the recognition accuracy of 83.6% and recall rate of 81.2%. The performance index is obviously better than the other three methods, which can meet the requirements of the actual operation of the harvesting robot.

In vitro and ex vivo relaxometric properties of ethylene glycol coated gadolinium oxide nanoparticles for potential use as contrast agents in magnetic resonance imaging

Magnetic nanoparticles (MNPs) have widely demonstrated their applicability in many biomedical applications including magnetic resonance imaging (MRI), hyperthermia, and drug delivery. However, the effectiveness of MNPs can be limited for in vivo applications due to their hydrophobic surfaces leading to nanoparticle agglomeration and thus requires appropriate surface modification to enhance colloidal stability. Glycols are widely used coating material for surface modifications of MNPs to improve their physicochemical properties and biocompatibility. The present work reports the preparation of two different sized ethylene glycol coated gadolinium oxide nanoparticles (EG@Gd2O3 NPs) using two different synthesis approaches and their applicability as contrast agents in MRI. Thermo-gravimetric analysis and Fourier transform infrared spectroscopy confirmed the successful coating of ethylene glycol on the surface of NPs. We found that independent of the size of NPs, the globular shaped EG@Gd2O3 NPs exhibited similar crystal structures, magnetic properties, and cellular cytotoxicity behavior. However, a significant impact of size on MRI contrast enhancement properties was seen. It was revealed that the relaxivity of EG@Gd2O3 NPs increases with a decrease in particle size. Small sized EG@Gd2O3 NPs (∼12 nm) exhibited a high specific in vitro and ex vivo longitudinal relaxivity of 3.7 and 1.5 mM−1 s−1, respectively, thus clearly elucidating the potential of these NPs for use as local contrast enhancement agents. The present study gives insights into the intrinsic dependence of magnetic resonance contrast effects of NPs on particle size and surface coating layer mass ratio and thus demonstrates the development of efficient magnetic nanoparticles based contrast agents by fine tuning of particle size and surface properties.

Local Contrast Enhancement based on Adaptive Logarithmic Mappings

Local Contrast Enhancement based on Adaptive Logarithmic Mappings

JSI-GAN: GAN-Based Joint Super-Resolution and Inverse Tone-Mapping with Pixel-Wise Task-Specific Filters for UHD HDR Video

Joint learning of super-resolution (SR) and inverse tone-mapping (ITM) has been explored recently, to convert legacy low resolution (LR) standard dynamic range (SDR) videos to high resolution (HR) high dynamic range (HDR) videos for the growing need of UHD HDR TV/broadcasting applications. However, previous CNN-based methods directly reconstruct the HR HDR frames from LR SDR frames, and are only trained with a simple L2 loss. In this paper, we take a divide-and-conquer approach in designing a novel GAN-based joint SR-ITM network, called JSI-GAN, which is composed of three task-specific subnets: an image reconstruction subnet, a detail restoration (DR) subnet and a local contrast enhancement (LCE) subnet. We delicately design these subnets so that they are appropriately trained for the intended purpose, learning a pair of pixel-wise 1D separable filters via the DR subnet for detail restoration and a pixel-wise 2D local filter by the LCE subnet for contrast enhancement. Moreover, to train the JSI-GAN effectively, we propose a novel detail GAN loss alongside the conventional GAN loss, which helps enhancing both local details and contrasts to reconstruct high quality HR HDR results. When all subnets are jointly trained well, the predicted HR HDR results of higher quality are obtained with at least 0.41 dB gain in PSNR over those generated by the previous methods. The official Tensorflow code is available at https://github.com/JihyongOh/JSI-GAN.

SR-ITM-GAN: Learning 4K UHD HDR With a Generative Adversarial Network

Currently, high dynamic range (HDR) videos with high resolution (HR) have become popular due to the display and the rendered technological advancements. However, making ultra-high definition (UHD) with HDR videos is expensive. The legacy low-resolution (LR) standard dynamic range (SDR) format is still largely used in practice. It is necessary to search for a solution to transform LR SDR videos into UHD HDR format. In this paper, we consider joint super resolution and learning inverse tone mapping an issue of high-frequency reconstruction and local contrast enhancement, and we propose an architecture based on a generative adversarial network to apply joint SR-ITM learning. Specifically, we include the residual ResNeXt block (RRXB) as a basic module to better capture high-frequency textures and adopt YUV interpolation to achieve local contrast enhancement. By adopting a generative adversarial network as a pivotal training mechanism, our designs show advantages in both integration and performance. Our code is now available on GitHub: SR-ITM-GAN.

轮廓波域内局部对比度增强的彩色图像灰度化算法

轮廓波域内局部对比度增强的彩色图像灰度化算法

A Novel and High-Speed Local Contrast Method for Infrared Small-Target Detection

Robust and effective small-target detection in infrared (IR) imagery has significant implications for modern military operations. For the objective of achieving simultaneous solid performance in detection rate, false alarm rate (FAR), and speed, this research provides a novel method making use of local contrast measure (LCM) based on high-boost filters [novel high-boost filter improved LCM (NHBF-ILCM)]. First, a novel high-boost filter is used for target signal enhancement and background clutter suppression, followed by an ILCM used to achieve high-speed local contrast and further target resolution enhancement and clutter suppression. Then, an adaptive threshold is used to segment the target. Finally, through experiments using three sequences with different and typically complex backgrounds, the detection capabilities of the proposed method are confirmed, and these results reveal that the proposed algorithm is able to achieve the best performance and highest efficiency for real IR small-target detection applications among similar algorithms.

Optimized Tone Mapping Function for Contrast Enhancement Considering Human Visual Perception System

Conventional contrast enhancement methods, including global and local enhancements, produce enhanced images with some limitations. Global contrast enhancement does not take the local characteristics into consideration, and therefore, the enhancement performance could be limited. On the other hand, a local contrast enhancement method achieves a noticeable improvement, but it generates unnatural improvement results compared with the input image. Due to the complementary characteristics of these two methods, it is hard to achieve remarkable contrast enhancement without visual artifacts. To overcome the limitations, we propose a new tone mapping function for contrast enhancement using an optimization approach that is subject to constraints such as the output image needing to be enhanced naturally and noticeably. Since contrast enhancement without artificiality is possible when the enhancement process mimics the human eye, we model the human visual perception system, and then, the model is incorporated into the proposed tone mapping function. Consequently, the contrast of the image is adaptively enhanced according to a region that is more attractive to a person. The experimental results demonstrate that the proposed algorithm outperforms other contrast enhancement methods in terms of both objective and subjective criteria.

Efficient multi-scale non-sub-sampled shearlet fusion system based on modified central force optimization and contrast enhancement

Infrared, visible, and medical image fusion systems are mandatory solutions for obtaining more spatial and more spectral information in a single image for efficient object detection and medical diagnosis applications. In this paper, an optimized fusion system for multi-modality images based on the Non-sub-Sampled Shearlet Transform (NSST) with Modified Central Force Optimization (MCFO), histogram matching, and local contrast enhancement is presented. The proposed multi-modality image fusion system consists of four stages. The first stage is the image registration and then histogram matching of one of the images to the other to allow the same dynamic range for both images to minimize the fusion artifacts. The NSST is used after that to decompose the images to be fused into their coefficients. The NSST provides a better sparse image representation of highly localized coefficients, anisotropic directionality, and reduced pseudo-Gibbs artifacts. After that, the MCFO technique is used to determine the optimum decomposition level and the optimum gain parameters for the best fusion of coefficients based on certain constraints. Finally, an additional contrast enhancement process is applied on the fused image to enhance its visual quality and reinforce details. The proposed fusion system is subjectively and objectively evaluated with different fusion quality metrics including average gradient, local contrast, standard deviation, edge intensity, entropy, Peak Signal-to-Noise Ratio (PSNR), and Qab/f. Real infrared, visible, and medical datasets of different modalities are used to test the proposed system. Experimental results demonstrate that the proposed system achieves a superior performance with higher image quality, higher evaluation metrics values, and much more details in images. These characteristics help for more accuracy in applications such as object detection and medical diagnosis.

Adaptive Contrast Enhancement for Infrared Images Based on the Neighborhood Conditional Histogram

In this paper, an adaptive contrast enhancement method based on the neighborhood conditional histogram is proposed to improve the visual quality of thermal infrared images. Existing block-based local contrast enhancement methods usually suffer from the over-enhancement of smooth regions or the loss of some details. To address these drawbacks, we first introduce a neighborhood conditional histogram to adaptively enhance the contrast and avoid the over-enhancement caused by the original histogram. Then the clip-redistributed histogram of the contrast-limited adaptive histogram equalization (CLAHE) is replaced by the neighborhood conditional histogram. In addition, the local mapping function of each sub-block is updated based on the global mapping function to further eliminate the block artifacts. Lastly, the optimized local contrast enhancement process, which combines both global and local enhanced results is employed to obtain the desired enhanced result. Experiments are conducted to evaluate the performance of the proposed method and the other five methods are introduced as a comparison. Qualitative and quantitative evaluation results demonstrate that the proposed method outperforms the other block-based methods on local contrast enhancement, visual quality improvement, and noise suppression.

ESTIMATING CROP DENSITY FROM MULTI-SPECTRAL UAV IMAGERY IN MAIZE CROP

Abstract. In this study we exploit UAV data for estimating Fractional Vegetation Cover (FVC) of maize crop at the early stages of the growing season. UAV survey with a MicaSense RedEdge multispectral sensor was carried out on July 13th, 2017 over a maize field in Italy; simultaneous RGB in situ pictures were collected to build a reference dataset of FVC over 15 ESU (Elementary Sampling Units) distributed over the field under investigation. The approach proposed for classification of UAV data is based on local contrast enhancement techniques applied to a vegetation index (NDVI-Normalized Difference Vegetation Index) to capture signal from small plants at the early development stage. The output fc map is obtained over grid cells over 70 × 70 cm size. The approach proposed here, based on contextual analysis, reduced artefacts due to illumination conditions by better enhancing signal from vegetation compared to, for example, simple band combination such as vegetation index alone (e.g. NDVI). Validation accomplished by a point comparison between estimated (from UAV) and in situ measured FVC values provided R2 = 0.73 and RMSE = 6%.

Hybrid bi-dimensional empirical mode decomposition based enhancement technique for extreme low contrast UAV thermal images

The performance of automatic target detection and classification systems are typically affected by reduced contrast quality introduced by external interferences. In particular for Unmanned Aerial Vehicle (UAV) captured thermal surveillance images, the effect is more evident. This advises the use of contrast enhancement technique as a solution to enhance the reduced contrast of hot regions for efficient target detection. In this paper, a simple and novel enhancement technique based on singular value decomposition (SVD) using Bi-Dimensional Empirical Mode Decomposition (BEMD) is proposed to enhance the hot regions in extreme low contrast thermal images captured by UAV. In the first step, the technique decomposes the thermal image into Intrinsic Mode Functions (IMFs) and residue by using BEMD. In the second step, it applies Contrast Limited Adaptive Histogram Equalization (CLAHE) in the residue for local contrast enhancement and then calculates the singular value matrix. In the third step, residue component is rescaled for further improvement of hot regions using scaling factor. In the fourth step, a detail enhanced IMF components are generated using gray scale transformation. Finally, the contrast enhanced residue undergoes Inverse BEMD (IBEMD) together with the detailed enhanced IMFs for enhanced image generation. Experimental results demonstrate that the proposed technique effectively enhances the contrast and details in the image with less visual artefacts than other state-of-the-art techniques.

Visibility improvement and mass segmentation of mammogram images using quantile separated histogram equalisation with local contrast enhancement

In this work, the authors develop a working software-based approach named ‘linearly quantile separated histogram equalisation-grey relational analysis’ for mammogram image (MI). This approach improves overall contrast (local and global) of given MI and segments breast-region with a specific end goal to acquire better visual elucidation, examination, and grouping of mammogram masses to help radiologists in settling on more precise choices. The fundamental commitment of this work is to demonstrate that results of good quality of breast-region segmentation can be accomplished from basic breast-region segmentation if the input image has good contrast and a better interpretation of hidden details. They have evaluated the proposed strategy for MIAS-MIs. Experimental results have shown that the proposed approach works better than state-of-the-art.

Machine learning hyperparameter selection for Contrast Limited Adaptive Histogram Equalization

Contrast enhancement algorithms have been evolved through last decades to meet the requirement of its objectives. Actually, there are two main objectives while enhancing the contrast of an image: (i) improve its appearance for visual interpretation and (ii) facilitate/increase the performance of subsequent tasks (e.g., image analysis, object detection, and image segmentation). Most of the contrast enhancement techniques are based on histogram modifications, which can be performed globally or locally. The Contrast Limited Adaptive Histogram Equalization (CLAHE) is a method which can overcome the limitations of global approaches by performing local contrast enhancement. However, this method relies on two essential hyperparameters: the number of tiles and the clip limit. An improper hyperparameter selection may heavily decrease the image quality toward its degradation. Considering the lack of methods to efficiently determine these hyperparameters, this article presents a learning-based hyperparameter selection method for the CLAHE technique. The proposed supervised method was built and evaluated using contrast distortions from well-known image quality assessment datasets. Also, we introduce a more challenging dataset containing over 6200 images with a large range of contrast and intensity variations. The results show the efficiency of the proposed approach in predicting CLAHE hyperparameters with up to 0.014 RMSE and 0.935 R2 values. Also, our method overcomes both experimented baselines by enhancing image contrast while keeping its natural aspect.

Image contrast expand enhancement system based on fuzzy theory

In the current age of information explosion, the extraction of relevant data from a large information pool, which includes images, has become crucial. Because of the easy availability of imaging devices, millions of images are being added to the image pool every day. Current image contrast enhancement methods have some drawbacks. First, for images captured in extreme lighting conditions, such as backlit images and extremely bright or dark images, extant image contrast enhancement methods cannot retain the brightness distribution details of the original image, resulting in image distortion. Second, the methods can only achieve global contrast enhancement but not local contrast enhancement. Third, these methods cannot satisfy the Human Visual System mapping curve, resulting in nonsmooth or distorted images. In this study, a novel image expand enhancement system based on fuzzy theory is proposed. This system has two major features: (1) the expand model is designed such that the correlations between light intensity and the intensity of an image captured in this light are considered to achieve local contrast enhancement, and (2) an extreme case of images processing step that is capable of enhancing. The proposed model has a fusion framework; therefore, the image fusion model can represent the relationship between the original image and the expanded image. Next, the fusion model describes some expected statistical properties of the desired intensity of the expand image. Thus, the details of the original image are retained. Experimental results revealed that the proposed algorithm is capable of adaptively enhancing the contrast of the original image while simultaneously extruding the details of objects in the image. The resulting enhanced image can be effectively used for image information analysis and other image-processing tasks.