Limited Adaptive Histogram Equalization Method Research Articles

Retinex Jointed Multiscale CLAHE Model for HDR Image Tone Compression

Tone-mapping algorithms aim to compress a wide dynamic range image into a narrower dynamic range image suitable for display on imaging devices. A representative tone-mapping algorithm, Retinex theory, reflects color constancy based on the human visual system and performs dynamic range compression. However, it may induce halo artifacts in some areas or degrade chroma and detail. Thus, this paper proposes a Retinex jointed multiscale contrast limited adaptive histogram equalization method. The proposed algorithm reduces localized halo artifacts and detail loss while maintaining the tone-compression effect via high-scale Retinex processing. A performance comparison of the experimental results between the proposed and existing methods confirms that the proposed method effectively reduces the existing problems and displays better image quality.

A novel intuitionistic fuzzy generator for low-contrast color image enhancement technique

Fuzzy logic systems play a significant role in various fields including image processing. Addressing the challenge of enhancing low-illumination images, which often contain a high degree of uncertain information, is a complex task. To overcome this issue, this study introduces a novel intuitionistic fuzzy generator for enhancing low-contrast images. Initially, a low-illumination image is considered and the image is then fuzzified using a fuzzification technique. The fuzzified image is converted into a intuitionistic fuzzy image using the proposed novel intuitionistic fuzzy generator and then applied a contrast limited adaptive histogram equalization method to obtain the enhanced image. The obtained results are compared with the existing studies. With the outcome of the performance measures such as entropy, no-reference low-light image enhancement evaluation and contrast improvement index, it is perceived that the proposed technique yields a finer results than the previous studies.

Classification of Pancreatic Cystic Lesions Using ResNet Deep Learning Network in Confocal Laser Endomicroscopy Videos

Accurate classification of pancreatic cystic lesions is crucial to differentiate mucinous lesions of malignant potential. We utilized the ResNet-50 and ResNet-101 network to develop a model for classification of the pancreatic cystic lesions. A total of 50 videos, 13,425 images, from five types of pancreatic cystic lesions and utilize the image rotation and contrast reversal scheme for the training. We adopt a contrast limited adaptive histogram equalization method onto the test video. Our method can automatically classify the feature type and record the prediction results frame by frame. The method has been evaluated on 18 test videos and achieves an accuracy 94% overall.

The Analysis of Underwater Imagery System for Armor Unit Monitoring Application

The placement of armor units for breakwaters in Indonesia is still done manually, which depends on divers in each placement of the armor unit. The use of divers is less effective due to limited communication between divers and excavator operators, making divers in the water take a long time. This makes the diver's job risky and expensive. This research presents a vision system to reduce the diver's role in adjusting the position of each armor unit. This vision system is built with two cameras connected to a mini-computer. This system has an image improvement process by comparing three methods. The results obtained are an average frame per second is 20.71 without applying the method, 0.45 fps for using the multi-scale retinex with color restoration method, 16.75 fps for applying the Contrast Limited Adaptive Histogram Equalization method, 16.17 fps for applying the Histogram Equalization method. The image quality evaluation uses the underwater color quality evaluation with 48 data points. The method that has experienced the most improvement in image quality is multi-scale retinex with color restoration. Forty data have improved image quality with an average of 14,131, or 83.33%. The number of images that experienced the highest image quality improvement was using the multi-scale retinex with color restoration method. Meanwhile, for image quality analysis based on Underwater Image Quality Measures, out of a total of 48 images, the method with the highest value for image quality is the contrast limited adaptive histogram equalization method. 100% of images have the highest image matrix value with an average value is 33.014.

Ensembling Scale Invariant and Multiresolution Gabor Scores for Palm Vein Identification

Biometric recognition based on palm vein trait has the advantages of liveness detection and high level of security. An improved human palm vein identification system based on ensembling the scores computed from scale invariant features and multiresolution adaptive Gabor features is proposed. In the training phase, from the input palm vein images, the interested palm regions are segmented using 3-valley point maximal palm extraction strategy, an improved method that extracts the maximal region of interest (ROI) easily and properly. Extracted ROI is enhanced using contrast limited adaptive histogram equalization method. From the enhanced image, local invariant features are extracted by applying scale invariant feature transform (SIFT). The texture and multiresolution features are extracted by employing adaptive Gabor filter over the enhanced image. These two features, scale invariant and multiresolution Gabor features act as the templates. In the testing phase, for the test images, ROI extraction, image enhancement, and two different feature extractions are performed. Using cosine similarity and match count-based classification, the score, Ss is computed for the SIFT features. Another score, Sg is computed using the normalized Hamming distance measure for the Gabor features. Both these scores are ensembled using the weighted sum rule to produce the final score, SF for identifying the person. Experiments conducted with CASIA multispectral palmprint image database version 1.0 and VERA palm vein database show that, the proposed method achieves equal error rate of 0.026% and 0.0205% respectively. For these databases, recognition rate of 99.73% and 99.89% respectively are obtained which is superior to the state-of-the-art methods in authentication and identification. The proposed work is suitable for applications wherein the authenticated person should not be considered as imposter.

Effectiveness of Feature Extraction by PCA-Based Detection and Naive Bayes Classifier for Glaucoma Images

After cataract, glaucoma is one of the second leading retinal diseases in the world. This paper presents the methodology to detect the glaucoma using principal component analysis. The images are involved in dilation as a preprocessing, enhancement using the contrast limited adaptive histogram equalization method, and followed by the extraction of features using principal component analysis. The extracted features are classified using support vector machine, Naive Bayes, and K-nearest neighbor classifiers. Comparing with other classifiers, the Naive Bayes provides high accuracy of 95% which demonstrates the effectiveness of the feature extraction and the classifier.

Underwater Image Enhancement Using Improved CNN Based Defogging

Due to refraction, absorption, and scattering of light by suspended particles in water, underwater images are characterized by low contrast, blurred details, and color distortion. In this paper, a fusion algorithm to restore and enhance underwater images is proposed. It consists of a color restoration module, an end-to-end defogging module and a brightness equalization module. In the color restoration module, a color balance algorithm based on CIE Lab color model is proposed to alleviate the effect of color deviation in underwater images. In the end-to-end defogging module, one end is the input image and the other end is the output image. A CNN network is proposed to connect these two ends and to improve the contrast of the underwater images. In the CNN network, a sub-network is used to reduce the depth of the network that needs to be designed to obtain the same features. Several depth separable convolutions are used to reduce the amount of calculation parameters required during network training. The basic attention module is introduced to highlight some important areas in the image. In order to improve the defogging network’s ability to extract overall information, a cross-layer connection and pooling pyramid module are added. In the brightness equalization module, a contrast limited adaptive histogram equalization method is used to coordinate the overall brightness. The proposed fusion algorithm for underwater image restoration and enhancement is verified by experiments and comparison with previous deep learning models and traditional methods. Comparison results show that the color correction and detail enhancement by the proposed method are superior.

A method for segmentation of tumors in breast ultrasound images using the variant enhanced deep learning

BackgroundBreast cancer is a deadly disease responsible for statistical yearly global death. Identification of cancer tumors is quite tasking, as a result, concerted efforts are thus devoted. Clinicians have used ultrasounds as a diagnostic tool for breast cancer, though, poor image quality is a major limitation when segmenting breast ultrasound. To address this problem, we present a semantic segmentation method for breast ultrasound (BUS) images. MethodThe BUS images were resized and then enhanced with the contrast limited adaptive histogram equalization method. Subsequently, the variant enhanced block was used to encode the preprocessed image. Finally, the concatenated convolutions produced the segmentation mask. ResultsThe proposed method was evaluated with two datasets. The datasets contain 264 and 830 BUS images respectively. Dice measure (DM), Jaccard measure, and Hausdroff distance were used to evaluate the methods. Results indicate that the proposed method achieves high DM with 89.73% for malignant and 89.62% for benign BUSs. Moreover, the results obtained validate the capacity of the proposed method to achieve higher DM in comparison with reported methods. ConclusionThe proposed algorithm provides a deep learning segmentation procedure that can segment tumors in BUS images effectively and efficiently.

HPWO-LS-based deep learning approach with S-ROA-optimized optic cup segmentation for fundus image classification

Recently, automated retinal image processing has been considered a competitive field of research due to the low-accuracy results, complexity, and unacceptable outcomes associated with it. In this article, we proposed a novel approach for the classification of fundus images from different kinds of fundus disorders. The original images are preprocessed in terms of noise and contrast enhancement by using the contrast limited adaptive histogram equalization method. The optic cup segmentation from the fundus images is effectively handled via the search and rescue optimization algorithm. After that, the color, texture, and shape-based gray-level co-occurrence matrix features are extracted. The hybrid particle swarm optimization with local search strategy improves the DNN parameter and the newly developed method is named as optimal DNN. The optimal DNN is used to classify whether the image is diabetic retinopathy, glaucoma, or age-related macular degeneration. Experimentally, different kinds of datasets such as STARE, Drishti, and RIM-One datasets with performance measure are validated. Finally, the proposed approaches demonstrate higher classification performances in terms of accuracy, specificity, sensitivity, precision, recall, and f-measure.

Performance analysis of automated lesion detection of diabetic retinopathy using morphological operation

Diabetic retinopathy is one of the most common complications of diabetes. Hemorrhages, micro-aneurysms, exudates are one of the earlier signs of diabetic retinopathy. This paper proposes an algorithm of matched filter with morphological operation for the detection of lesions in the fundus retinal image. Contrast limited adaptive histogram equalization method is used for the extraction of vessels. The noise is removed from the images using matched filter. After enhancement, the thresholding is applied for vessel extraction. The threshold of the image is done by the iterative self-organizing data analysis technique algorithm method. After removal of optic disk and the blood vessels from the retinal image, the morphology method is used to easily identify different types of lesions. The diabetic retinopathy images were collected from DIARETDB1; the morphology operation method is analyzed using the metrics of sensitivity, specificity and accuracy. The proposed method's detection accuracy value for the recognition of micro-aneurysms, exudates and hemorrhages was 98.43%, 98.06% and 98.68% compared with the results of the differential evolution algorithm. Detection of lesion such as micro-aneurysms, hemorrhages and exudates was possible. When compared with the differential evolution algorithm, morphological method achieved good accuracy for the detection of diabetic retinopathy.

Misty Image Repair System with Dark Channel Prior and CLAHE

The aim of this study is to remove the influence of weather factors in order to improve the visual effects of the image and provide benefit to post-processing. Images captured in hazy or foggy weather conditions can be seriously degraded by scattering of atmospheric particles, which reduces the contrast, changes the color, and makes the object features difficult to identify by human vision and by some outdoor computer vision systems. Therefore image dehazing is an important issue and has been widely researched in the field of computer vision. Image haze removal based on dark channel prior can perform very well. But it didn’t deal with sky region and brightness objects because dark channel prior is not established in these regions. To solve the problem and enhance the contrast, this paper propose to restored the hazy image by adding the contrast limited adaptive histogram equalization. With the contrast limited adaptive histogram equalization method, the results of the dark channel prior results can be contrasted so that the image becomes clearer and more specific. Experimental results showed that such method is MSE 0.256 is equal to PSNR of 54,043 With this research, it is expected that a clean image after processing can provide maximum information to humans.

Morphological Enhancement of Microcalcifications in Digital Mammograms

Mammography is a commonly used technique for early detection of breast cancer. In mammograms, microcalcifications show low contrast margin with the background parenchymal tissue (specifically when the background tissue type is fibroglandular) as a result, subjective analysis of these calcifications with respect to their size, shape and morphology presents a daunting challenge even for experienced radiologists. Thus the present work investigates the potential of two morphological techniques i.e., top-hat morphological processing and h-dome morphological processing for enhancement of microcalcifications embedded in variety of background tissue types including fatty, glandular and fibroglandular tissues while restoring their shape and size. The enhancement results are also compared with standard contrast limited adaptive histogram equalization method. For subjective analysis, 25 synthetic images with simulated microcalcifications of various shapes and sizes are used. Objective analysis is carried out on 50 mammographic images taken from benchmark dataset (McGill University mammographic database) by computing quantitative indices like contrast improvement ratio and detail variance/background variance ratios. After rigorous experimentation on both synthetic and benchmark data set it was observed that h-dome morphological processing (with h = 60) is ideally suited for enhancement of microcalcifications while restoring their shape and size.