Local Contrast Enhancement Research Articles

A two-stage histogram equalization enhancement scheme for feature preservation in retinal fundus images

Early detection of ophthalmologic complications in the retinal fundus images is crucial and requires a quality image for accurate analysis and diagnosis. This often proves tricky due to the visual complexities of some retinal fundus images resulting in over-enhancement, non-uniform contrast, artificial boundaries, abrupt colour change, and other undesirable limitations in the performance of the existing image enhancement methods. In this paper, the uneven illumination and colour balance are globally adjusted using a histogram match. At the same time, the luminance component (V) of the HSV colour space is equalized with CLAHE for local contrast enhancement. Eight control parameter values are investigated to choose the most suitable values to restrict excessive enhancement and control the quality of the enhanced image. The performance of the proposed enhancement algorithm on the STARE, DRIVE, HRF, and DIARETDB1 databases shows that the method can prominently enhance colour retinal fundus images. The visual and quantitative comparisons of the proposed method with state-of-the-art techniques are remarkably outstanding. The statistical analysis test achieves a ρ-value far<0.05 justifying the statistical significance of the quantitative metrics and the high-quality performance of our enhancement framework.

An Improved Detection Algorithm for Ischemic Stroke NCCT Based on YOLOv5.

Cerebral stroke (CS) is a heterogeneous syndrome caused by multiple disease mechanisms. Ischemic stroke (IS) is a subtype of CS that causes a disruption of cerebral blood flow with subsequent tissue damage. Noncontrast computer tomography (NCCT) is one of the most important IS detection methods. It is difficult to select the features of IS CT within computational image analysis. In this paper, we propose AC-YOLOv5, which is an improved detection algorithm for IS. The algorithm amplifies the features of IS via an NCCT image based on adaptive local region contrast enhancement, which then detects the region of interest via YOLOv5, which is one of the best detection algorithms at present. The proposed algorithm was tested on two datasets, and seven control group experiments were added, including popular detection algorithms at present and other detection algorithms based on image enhancement. The experimental results show that the proposed algorithm has a high accuracy (94.1% and 91.7%) and recall (85.3% and 88.6%) rate; the recall result is especially notable. This proves the excellent performance of the accuracy, robustness, and generalizability of the algorithm.

Adaptive CFAR Method for SAR Ship Detection Using Intensity and Texture Feature Fusion Attention Contrast Mechanism.

Due to the complexity of sea surface environments, such as speckles and side lobes of ships, ship wake, etc., the detection of ship targets in synthetic aperture radar (SAR) images is still confronted with enormous challenges, especially for small ship targets. Aiming at the key problem of ship target detection in the complex environments, the article proposes a constant false alarm rate (CFAR) algorithm for SAR ship target detection based on the attention contrast mechanism of intensity and texture feature fusion. First of all, the local feature attention contrast enhancement is performed based on the intensity dissimilarity and the texture feature difference described by local binary pattern (LBP) between ship targets and sea clutter, so as to realize the target enhancement and background suppression. Furthermore, the adaptive CFAR ship target detection method based on generalized Gamma distribution (GΓD) which can fit the clutter well by the goodness-of-fit analyses is carried out. Finally, the public datasets HRSID and LS-SSDD-v1.0 are used to verify the effectiveness of the proposed detection method. A large number of experimental results show that the proposed method can suppress clutter background and speckle noise and improve the target-to-clutter rate (TCR) significantly, and has the relative high detection rate and low false alarm rate in the complex background and multi-target marine environments.

Adaptive Reversible Data Hiding With Contrast Enhancement Based on Multi-Histogram Modification

Reversible data hiding with contrast enhancement (RDH-CE) is proposed to aim at improving the contrast of images while embedding data. After deeply analyzing and studying the RDH-CE method proposed by Jafar <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , it is found that there are three main problems in their method. Firstly, their method ignores the fact that the left-bottom neighbors of a pixel contribute to increasing the accuracy of the local-complexity evaluation. Secondly, Jafar <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> ’s method employs K-means clustering in combination with one single feature to split pixels into five classes, leading to a weak clustering performance. Finally, Jafar <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> ’s method uniformly embedded 1 bit into each pixel irrespective of the local complexity, and thus, the embedding capacity is limited. To this end, an improved RDH-CE method is proposed in this paper. Considering that the complexity evaluation plays a vital role in both contrast enhancement and payload increase, we improve embedding performance by including left-bottom neighbors of a pixel into complexity evaluation. Compared with one single feature in Jafar <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> ’s method, we extract multiple features to assist K-means clustering such that a better cluster performance is obtained. In addition, our method provides an adaptive pixel modification strategy based on the local complexity, in which we can adaptively embed 1 or 2 bits into a pixel according to the corresponding complexity. By these three improvements, our method is capable of achieving high capacity while enhancing contrast. The experimental results also show that our method achieves higher accuracy of the complexity evaluation, larger payload, and better local contrast enhancement than those existing RDH-CE related methods.

Reversible Data Hiding with a New Local Contrast Enhancement Approach

Reversible data hiding schemes hide information into a digital image and simultaneously increase its contrast. The improvements of the different approaches aim to increase the capacity, contrast, and quality of the image. However, recent proposals contrast the image globally and lose local details since they use two common methodologies that may not contribute to obtaining better results. Firstly, to generate vacancies for hiding information, most schemes start with a preprocessing applied to the histogram that may introduce visual distortions and set the maximum hiding rate in advance. Secondly, just a few hiding ranges are selected in the histogram, which means that just limited contrast and capacity may be achieved. To solve these problems, in this paper, a novel approach without preprocessing performs an automatic selection of multiple hiding ranges into the histograms. The selection stage is based on an optimization process, and the iterative-based algorithm increases capacity at embedding execution. Results show that quality and capacity values overcome previous approaches. Additionally, visual results show how greyscale values are better differentiated in the image, revealing details globally and locally.

A contrast enhancement framework under uncontrolled environments based on just noticeable difference

Image contrast enhancement refers to an operation of remapping the pixels’ values of an image to emphasize desired information in the image. In this work, we propose a novel pixel-based (local) contrast enhancement algorithm, based on the human visual perception. First, we make an observation that pixels with lower regional contrast should be amplified for the purpose of enhancing the contrast and pixels with higher regional contrast should be suppressed to avoid undesired over-enhancement. To determine the quality of the regional contrast in the image (either lower or higher), a reference image will be created using a proposed global based contrast enhancement method (termed as Mean Brightness Bidirectional Histogram Equalization in the paper) for fast computation reason. To quantify the abovementioned regional contrast, we propose a method based on human visual perception taking Just Noticeable Difference (JND) into account. In short, our proposed algorithm is able to limit the enhancement of well-contrasted regions and enhance the poor contrast regions in an image. Both objective quality and subjective quality experimental results suggested that the proposed algorithm enhances images consistently across images with different dynamic range. We conclude that the proposed algorithm exhibits excellent consistency in producing satisfactory result for different type of images. It is important to note that the algorithm can be directly implemented in color space and not limited only to grayscale. The proposed algorithm can be obtained from the following GitHub link: https://github.com/UTARSL1/CHE.

GLOBAL CONTRAST ENHANCEMENT USING SMI &amp; PR ALGORITHMS

Image enhancement is one of the challenging issues in low level image processing. In general, it is difficult to design a visual artifact free contrast enhancement method. Considering this, we propose a global, computationally efficient spatial contrast enhancement method which performs enhancement by considering the spatial locations of gray-levels of an image instead of direct use of gray-levels or their co-occurrences. Contrast enhancement is the important factor in image enhancement. Contrast enhancement is used to increase the contrast of an image with low dynamic range and bring out the image details that would be hidden. The enhanced image is looks qualitatively better than the original image if the gray-level differences. This work proposes a novel algorithm, which enhances the low contrast input image by using the spatial information of pixels. This algorithm introduces new method to compute spatial entropy of pixels using spatial distribution of gray levels. This is different than the conventional methods, this algorithm considers the distribution of spatial locations of gray levels of an image instead of gray level distribution or joint statistics computed from gray levels of an image. For each gray level the corresponding spatial distribution is computed by considering spatial location of all pixels having the same gray level in histogram. From the spatial distribution of gray levels of an image entropy can be measured and create distribution which can be further mapped to uniform distribution function to achieve final contrast enhancement. This method achieves contrast enhancement of low contrast image without altering the image if the image’s contrast is high enough. This algorithm considers transform domain coefficient weighting to achieve global and local contrast enhancement of the image. Experimental results show that proposed algorithm produces better enhanced images than existing algorithms.

Color Correction and Local Contrast Enhancement for Underwater Image Enhancement

Underwater images suffer from various quality degradation problems such as color cast, low contrast, and blurred details. To solve these issues, a novel underwater image enhancement method that can implement color correction, detail sharpening, and contrast enhancement in stages. In particular, the proposed method combines multi-channel color compensation with color correction. It solves detail blurring and low contrast by the Gaussian differential pyramid and the local contrast enhancement of contrast limited adaptive histogram equalization, respectively. The proposed method mainly includes color compensation, color correction, detail sharpening, and contrast enhancement. Qualitative and quantitative comparisons demonstrate that the proposed method can effectively remove the blur of the image, realize the color correction, and significantly improve the clarity of the image.

Contrast enhancement based on reflectance-oriented probabilistic equalization

Despite advances in contrast enhancement, it remains difficult to adaptively improve both the global contrast (base structures) and the local contrast (detailed textures) of an image. To solve this problem, we first propose a novel 2D histogram equalization approach called ROPE for global contrast enhancement. ROPE assumes intensity occurrence and co-occurrence to be dependent on each other and newly estimates the distribution of intensity occurrence (1D histogram) by marginalizing over the distribution of intensity co-occurrence (2D histogram). The 2D histogram is generated by incorporating local reflectance differences into the density estimation, so that ROPE can provide sufficient brightness enhancement not only for normal-light images but also for dark images. For local contrast enhancement, ROPE is extended to compute locally adaptive 2D histograms, giving birth to a novel approach called AdaROPE. Extensive experiments on four public datasets consisting of 8400 images demonstrate the superiority of our approaches over state-of-the-art image enhancement.

Analysis of accuracy factor and pre-processing methodology of image compensation for 3D reconstruction using 2D image obtained from unmanned aerial vehicle (UAV)

ABSTRACT Unmanned aerial vehicles (UAVs) are widely utilized in civil engineering due to the ease of use of UAVs in aerial monitoring tasks. A UAV-based mapping system is efficient and allows for a higher frequency of monitoring. Furthermore, improving technologies with increasing applications has made it possible to wirelessly send auto flight data to ground stations in real time. Existing studies have focused on data processing on UAV captured data to a form that is measurable and manageable. Studies on using 2D images for 3D modeling usually employ the point cloud generation algorithm. This research explores a 2D image environment that is favorable for point cloud generation by adjusting the environmental values of the image that have a direct influence on the algorithm of point cloud generation. Accuracy analysis is done by checking the number of generated point clouds by variation of illumination values. The aim of this study is to propose a pre-processing methodology to adjust 2D image using histogram equalization, adaptive histogram equalization, image sharpening and local contrast enhancement method in construction site. 2D image corrections by pre-processing methods are converted into point cloud data and results performance is analyzed and verified by comparing with original data results.

An Early Breast Cancer Detection System Using Stacked Auto Encoder Deep Neural Network with Particle Swarm Optimization Based Classification Method

The demand in breast cancer’s early detection and diagnosis over the last few decade has given a new research avenues. For an individual who is suffered from breast cancer, a successful treatment plan can be specified if early stage diagnosis of non-communicable disease is done as stated by world health organization (WHO). Around the world, mortality can be reduced by cure disease’s early diagnosis. For breast cancer’s early detection and to detect other abnormalities of human breast tissue, digital mammogram is used as a most popular screening method. Early detection is assisted by periodic clinical check-ups and self-tests and survival chance is significantly enhanced by it. For mammograms (MGs), deep learning (DL) methods are investigated by researchers due to traditional computer-aided detection (CAD) systems limitations and breast cancer’s early detection’s extreme importance and patients false diagnosis high impact. So, there is need to have a noninvasive cancer detection system which is efficient, accurate, fast and robust. There are two process in proposed work, Histogram Rehabilitated Local Contrast Enhancement (HRLCE) technique is used in initial process for contrast enhancement with two processing stages. Contrast enhancements potentiality is enhanced while preserving image’s local details by this technique. So, for cancer classification, Particle Swarm Optimization (PSO) and stacked auto encoders (SAE) combined with framework based on DNN called SAE-PSO-DNN Model is used. The SAE-DNN parameters with two hidden layers are tuned using PSO and Limited-memory BFGS (LBFGS) is used as a technique for reducing features. Specificity, sensitivity, normalized root mean square erro (NRMSE), accuracy parameters are used for evaluating SAE-PSO-DNN models results. Around 92% of accurate results are produced by SAE-PSO-DNN model as shown in experimentation results, which is far better than Convolutional Neural Network (CNN) as well as Support Vector Machine (SVM) techniques.

Dynamic stochastic resonance and image fusion based model for quality enhancement of dark and hazy images

Haze/fog is a common weather phenomenon that may exist in both day and night conditions and presents loss of contrast in captured images. For an imaging device to capture good quality photographs, it is necessary that the captured scene be well-illuminated. But sometimes due to bad illumination, which may be due to natural or manmade conditions, the captured images present degradation. To solve the ill effects of bad illumination, we propose an image enhancement algorithm that sufficiently deals with both hazy and dark images. By combining dynamic stochastic resonance and a fusion method based on illumination estimation, a local contrast enhancement of the image as seen in the fused image is achieved in the first stage. While images that are only dark are sufficiently illuminated, the effect of haze in day/night hazy images remains unchanged. In the second stage, we solve this issue by modifying the atmospheric degradation model to reduce the effect of haze. The proposed method does not rely on the estimation of atmospheric light; rather, a mean preserving algorithm to restore the mean brightness of the image has been applied. The qualitative and quantitative experiments validate the efficacy and robustness of the proposed method.

Mosaic, block-face microscopy for measuring cell dimensions, cell collapse, and spatial relationships in wood

Summary A conventional stereo light microscope was used to image polished wood surfaces at cellular resolution over size scales of the growth ring or larger. Bandpass filtering and local area contrast enhancement were used to aid automatic image thresholding and binarisation. An estimate for the location and proportion of cell collapse was introduced based on the distance between uncollapsed cell lumens. Additionally, spatial associations between vessels were determined using a Euclidean distance transform. The analysis of pith to bark cores provided sufficient detail to show significant intra and inter-annual trends in Pinus radiata tracheid dimensions (wall thickness, wall area, and radial widths). These trends were consistent with expectations and in agreement with the literature. Measured cell dimensions may be influenced by cell collapse and deformation as a result of drying. The analysis of air, kiln and oven-dried Eucalyptus nitens showed that cell collapse was highly variable but generally more prominent in the outer third of growth rings. There were significant changes in vessel shape across the growth rings and vessel area was significantly reduced by drying. The technique provides an intermediate step between detailed microscopy and macroscopic imaging that allows spatial analysis at the wood cell level.

Variational Anisotropic Gradient-Domain Image Processing.

Gradient-domain image processing is a technique where, instead of operating directly on the image pixel values, the gradient of the image is computed and processed. The resulting image is obtained by reintegrating the processed gradient. This is normally done by solving the Poisson equation, most often by means of a finite difference implementation of the gradient descent method. However, this technique in some cases lead to severe haloing artefacts in the resulting image. To deal with this, local or anisotropic diffusion has been added as an ad hoc modification of the Poisson equation. In this paper, we show that a version of anisotropic gradient-domain image processing can result from a more general variational formulation through the minimisation of a functional formulated in terms of the eigenvalues of the structure tensor of the differences between the processed gradient and the gradient of the original image. Example applications of linear and nonlinear local contrast enhancement and colour image Daltonisation illustrate the behaviour of the method.

Small moving vehicle detection via local enhancement fusion for satellite video

ABSTRACT Satellite video is an emerging data source for dynamic Earth observation, which provides us with a new means for large-scale moving vehicle detection and traffic monitoring. However, the ratio of the foreground to background in satellite video is severely uneven. Furthermore, due to the high altitude and spatial resolution of video satellites, the moving vehicles often have a small size and low contrast with the background. There is also complex environment and background movement due to the imaging mode of satellite video, which makes the vehicle detection more challenging. In this paper, to solve the problems of the low contrast and the dynamic background in satellite video, a local enhancement fusion Gaussian mixture model and improved three-frame difference detection framework, namely E-GMMTFD, is proposed to detect small moving vehicles in satellite video. In the E-GMMTFD framework, the local contrast enhancement method is used to improve the contrast between the target and the background. The Gaussian mixture model (GMM) and the three-frame difference (TFD) method are fused by a logical operation to detect moving vehicles. The E-GMMTFD framework utilizes the adaptability to complex environment changes of the GMM and the strength of the TFD method to eliminate background movement, thus reducing the false alarms caused by illumination change and background movement. Three experiments were performed on moving vehicle detection using Jilin-1 satellite video and SkySat-1 satellite video. The experimental results confirmed that the proposed framework can be efficiently used to detect moving vehicles in the large scale of satellite video. Based on the vehicle detection results for the satellite video, the traffic density of the urban road network can provide technical support for alleviating urban traffic congestion.

A small target detection algorithm in infrared image by combining multi-response fusion and local contrast enhancement

The detection of small target in infrared (IR) images is the key technology for IR search, tracking and early warning. When the target locates at distance, the target occupies tiny area, shows low signal-to-noise ratio, which is hard to be detected. An infrared small target detection algorithm by combining multi-response fusion and local contrast enhancement (MRF-LCE) is introduced in this paper. First, Laplacian of Gaussian (LoG) filter and negative LoG filter are utilized to obtain the response map of bright and dark targets, respectively. Second, a multi-response fusion method is designed to respond to the bright and dark targets, simultaneously. Finally, a local contrast method is implemented to further enhance the targets and suppress the background, and these small targets are segmented by an adaptive threshold method. Experiments on three different real sequences show that the proposed algorithm can effectively detect targets in different scenarios.

Morphological Transformation In Poor Lighting Images For Image Contrast Enhancement

The paper presents a novel algorithm for the computation of the image decomposition using a morphological filter with reconstruction. The target applications are image contrast enhancement especially those with high dynamic content. Both bright and dark regions contrast enhancement were considered. A new hardware efficient implementation of decomposition is presented. Following decomposition in 5 levels of detail a local contrast enhancement is performed. The new reconstruction algorithm and its hardware implementation as proposed is shown to be independent on structural element size and that it results in a predictable time frame operation. A mixed schematic and VHDL/Verilog description of the decomposition filters was synthesized and results show far higher speed performance compared with solutions found in recent literature.&#x0D; Image organizing strategies are essentially required to the pictures captured by satellites. The grey levels in the image captured by satellites need to be normalized for improving picture contrast. The pictures captured by satellites are generally poor in quality and the contrast levels are also very less. In this manuscript a differentiation approach dependent on scientific morphology called rotational morphological transformations are proposed using Modified Opening by Reconstruction Method (MORM). This methodology upgrade pictures with poor contrast and unable to recognize the objects in the image. In the images with poor lightening, morphological operations are performed to improve its clarity and contrast. Picture enhancement has been done by applying morphological operations on the satellite images considered. The technique utilizes data from picture by squares, while the morphological methods change the strategy using the contrast enhancement activity, which is utilized to characterize the multi-foundation poor lightening pictures. The total picture handling procedure is implemented utilizing MATLAB reproduction model. The MORM method is compared with the traditional methods and the results show that the proposed method is better in improving the accuracy rate.

An automated classification of HEp-2 cellular shapes using Bag-of-keypoint features and Ant Colony Optimization

In this study, an attempt has been made to differentiate HEp-2 cellular shapes using Bag-of-keypoint features and optimization. For this, the images are considered from a publicly available database. To increase the cell structure visibility, the images are pre-processed using edge-sensitive local contrast enhancement. Further, the Speeded-up Robust Feature (SURF) keypoints are extracted and Bag-of-keypoints for each shape are generated. These features are subjected to Ant Colony Optimization (ACO) algorithm for feature selection. The optimal features obtained are then fed to Support Vector Machine (SVM) and k-Nearest Neighbour (kNN) classifiers. Results show that the ACO algorithm can identify the optimal features that characterize the cellular shapes. SVM and kNN are able to differentiate between the shapes with an average classification accuracy of 93.6% and 94.8% respectively. Since differential diagnosis of HEp-2 cellular shapes is significant in the disease-specific prognosis and treatment, this study seems to be clinically relevant.

Peri-ictal magnetic resonance imaging characteristics in dogs with suspected idiopathic epilepsy.

BackgroundThe pathophysiology of changes in magnetic resonance imaging (MRI) detected after a seizure is not fully understood.ObjectiveTo characterize and describe seizure‐induced changes detected by MRI.AnimalsEighty‐one client‐owned dogs diagnosed with idiopathic epilepsy.MethodsData collected retrospectively from medical records and included anatomical areas affected, T1‐, T2‐weighted and T2‐FLAIR (fluid‐attenuated inversion recovery) appearance, whether changes were unilateral or bilateral, symmetry, contrast enhancement, mass effect, and, gray and white matter distribution. Diffusion‐ and perfusion weighted maps were evaluated, if available.ResultsSeizure‐induced changes were T2‐hyperintense with no suppression of signal on FLAIR. Lesions were T1‐isointense (55/81) or hypointense (26/81), local mass effect (23/81) and contrast enhancement (12/81). The majority of changes were bilateral (71/81) and symmetrical (69/71). The most common areas affected were the hippocampus (39/81) cingulate gyrus (33/81), hippocampus and piriform lobes (32/81). Distribution analysis suggested concurrence between cingulate gyrus and pulvinar thalamic nuclei, the cingulate gyrus and parahippocampal gyrus, hippocampus and piriform lobe, and, hippocampus and parahippocampal gyrus. Diffusion (DWI) characteristics were a mixed‐pattern of restricted, facilitated, and normal diffusion. Perfusion (PWI) showed either hypoperfusion (6/9) or hyperperfusion (3/9).Conclusions and Clinical ImportanceMore areas, than previously reported, have been identified that could incur seizure‐induced changes. Similar to human literature, DWI and PWI changes have been identified that could reflect the underlying metabolic and vascular changes.

Underwater image enhancement using contrast limited adaptive histogram equalization and layered difference representation

Underwater images, which have low contrast and visibility as a result of selective attenuation based on the wavelength of the light passing through water, needs some corrections to extract meaningful information from them. In this paper, we aim to combine two different approaches; global and local contrast enhancement techniques, to obtain better visual quality while enhancing image contrasts on underwater images. While global technique (LDR) ensures the overall enhancement of the image, local technique (CLAHE) considers local brightness features of the image in RGB color space. The proposed method also applies local color correction on underwater image. While methods in the literature apply various approaches on the global histogram of channels, our method divides underwater image into non-overlapping sub-blocks and apply histogram equalization on them. The method uses HSV color space and especially S, V components for color correction. The results of the qualitative analysis show that it produces very good images, in contrast, color, and detail compared to other enhancement methods. The proposed method also decreases the effect of under- and over-enhanced areas and the blue-green effect on the output image. However, the visibility of the objects in the images are increased by color correction. For quantitative analysis, the proposed method produces the highest average value of entropy (7.83), EMEE (32.06), EME (40.97), average gradient (152.55), and Sobel count (130393) for 200 underwater images.