Automatic Contrast Enhancement Research Articles

Reversible data hiding based on global and local automatic contrast enhancement of low-light color images

Currently, many scholars have investigated reversible data hiding (RDH) based on automatic contrast enhancement (ACE). For RDH based on ACE (ACERDH), various methods have been proposed on how to better improve image quality. Preserving brightness can prevent the image from being over-enhanced. However, some ACERDH methods that can preserve brightness well cannot sufficiently enhance low-light images. Although some ACERDH methods that cannot preserve brightness well can more sufficiently enhance low-light color images, they lack the consideration of the local region, which is not conducive to enhancing the contrast of the local region. Therefore, a novel RDH method based on global ACE and local ACE is proposed. The global contrast is first improved by equalizing the global histogram. Then the high complexity region is optimized by the proposed RDH method based on double-layer ACE to further enhance the local contrast. The low-light color image can be well enhanced by applying the proposed method to enhance the R, G, and B channels sequentially. Experimental results demonstrated that the proposed method is superior to some existing advanced methods in obtaining better image quality and hiding more secret data.

Reversible data hiding with automatic contrast enhancement for color images

Automatic contrast enhancement (ACE) is a technique that can automatically enhance the image contrast. Reversible data hiding (RDH) with ACE (ACERDH) can achieve ACE while hiding data. However, some methods with good performance for color images suffer from insufficient enhancement. Therefore, an ACERDH method based on the R, G, B, and V channels enhancement is proposed. First, histogram shifting with contrast control is proposed to enhance the R, G, and B channels. It can prevent contrast degradation and histogram shifting from stopping prematurely. Then, the V channel is enhanced. Since some RDH methods with non-ACE that can well enhance the V channel have a low automation level, histogram shifting with brightness control that can realize ACE very well is proposed. It can effectively avoid over-enhancement by controlling the brightness. Experimental results verify that the proposed method improves the image quality and embedding capability better than some state-of-the-art methods.

Distortion Correction and Denoising of Light Sheet Fluorescence Images.

Light Sheet Fluorescence Microscopy (LSFM) has emerged as a valuable tool for neurobiologists, enabling the rapid and high-quality volumetric imaging of mice brains. However, inherent artifacts and distortions introduced during the imaging process necessitate careful enhancement of LSFM images for optimal 3D reconstructions. This work aims to correct images slice by slice before reconstructing 3D volumes. Our approach involves a three-step process: firstly, the implementation of a deblurring algorithm using the work of K. Becker; secondly, an automatic contrast enhancement; and thirdly, the development of a convolutional denoising auto-encoder featuring skip connections to effectively address noise introduced by contrast enhancement, particularly excelling in handling mixed Poisson-Gaussian noise. Additionally, we tackle the challenge of axial distortion in LSFM by introducing an approach based on an auto-encoder trained on bead calibration images. The proposed pipeline demonstrates a complete solution, presenting promising results that surpass existing methods in denoising LSFM images. These advancements hold potential to significantly improve the interpretation of biological data.

Adaptive gamma correction for automatic contrast enhancement of Chest-X-ray images affected by various lung diseases

Adaptive gamma correction for automatic contrast enhancement of Chest-X-ray images affected by various lung diseases

Yeast cell detection using fuzzy automatic contrast enhancement (FACE) and you only look once (YOLO)

In contemporary biomedical research, the accurate automatic detection of cells within intricate microscopic imagery stands as a cornerstone for scientific advancement. Leveraging state-of-the-art deep learning techniques, this study introduces a novel amalgamation of Fuzzy Automatic Contrast Enhancement (FACE) and the You Only Look Once (YOLO) framework to address this critical challenge of automatic cell detection. Yeast cells, representing a vital component of the fungi family, hold profound significance in elucidating the intricacies of eukaryotic cells and human biology. The proposed methodology introduces a paradigm shift in cell detection by optimizing image contrast through optimal fuzzy clustering within the FACE approach. This advancement mitigates the shortcomings of conventional contrast enhancement techniques, minimizing artifacts and suboptimal outcomes. Further enhancing contrast, a universal contrast enhancement variable is ingeniously introduced, enriching image clarity with automatic precision. Experimental validation encompasses a diverse range of yeast cell images subjected to rigorous quantitative assessment via Root-Mean-Square Contrast and Root-Mean-Square Deviation (RMSD). Comparative analyses against conventional enhancement methods showcase the superior performance of the FACE-enhanced images. Notably, the integration of the innovative You Only Look Once (YOLOv5) facilitates automatic cell detection within a finely partitioned grid system. This leads to the development of two models—one operating on pristine raw images, the other harnessing the enriched landscape of FACE-enhanced imagery. Strikingly, the FACE enhancement achieves exceptional accuracy in automatic yeast cell detection by YOLOv5 across both raw and enhanced images. Comprehensive performance evaluations encompassing tenfold accuracy assessments and confidence scoring substantiate the robustness of the FACE-YOLO model. Notably, the integration of FACE-enhanced images serves as a catalyst, significantly elevating the performance of YOLOv5 detection. Complementing these efforts, OpenCV lends computational acumen to delineate precise yeast cell contours and coordinates, augmenting the precision of cell detection.

Reversible data hiding based on automatic contrast enhancement using histogram expansion

A lot of Reversible Data Hiding (RDH) methods aim to generate a stego image infinitely approaches the original image while the quality of the original image is leaved out of consideration. Juxtaposed with a plain image, a contrast enhanced version always improves the user experience significantly. Reversible Data Hiding with Contrast Enhancement (RDHCE) enhances the stego image contrast combined with its payloads and enables the cover image to be regained accurately after the payloads have been extracted. This study presents a novel RDHCE method using histogram expansion. First, a new local histogram selecting strategy is proposed to improve the contrast of the whole image. Meanwhile, the global average brightness is used as a reference to determine the shifting direction of the local histogram to prevent the image from being over-enhanced. Moreover, the contrast can be improved adaptively when a reasonable number of data is embedded at the selected embedding points. Experimental results show that, with a given payload, the proposed method achieves better contrast and maintains good visual quality compared with state-of-the-arts.

Automatic contrast enhancement with reversible data hiding using bi-histogram shifting

Aiming at resolving artificial distortions in marked images and increasing embedding capacity, this paper presents an automatic contrast enhancement (CE) method with reversible data hiding (RDH) using bi-histogram shifting (BHS). Current CE methods introduce distortions in marked images as embedding capacity increases. This paper focuses on resolving these artificial distortions by achieving better CE and increasing embedding capacity in marked images. The proposed method adaptively selects two peak bins and two lower bins based on four conditions depending on image brightness using the brightness preservation (BP) method. The two peak bins are used as splitting and embedding bins, whereas the lower bins are used as combining bins. The proposed method pairs each peak bin with a lower bin and performs BHS in the opposite direction to achieve histogram equalization (HE) while embedding secret data in marked images. The proposed method is compared with existing methods and evaluated with PSNR, SSIM, RCE, REE, RMBE, and VIF quality assessment metrics. Experimental results show that the proposed method's marked images are visually and qualitatively superior to existing works, with an average increase in embedding capacity of 37.6%. The proposed method is reversible and performs the extraction of embedded data to retrieve the original images losslessly.

Reversible data hiding with automatic contrast enhancement using two-sided histogram expansion

Recently, reversible data hiding (RDH) has emerged into a new class of data hiding methods that enables exact retrieving of both embedded data and cover medium. In the present study, a novel automatic RDH method with contrast enhancement is proposed, in which the data is embedded through two-sided histogram expansion. Two-sided histogram shifting doubles the number of bits embedded at each iteration. Moreover, it preserves the mean brightness of the cover image and prevents it from over enhancement with less calculation. Experimental results on two sets of images show that the proposed method enhances the image contrast at an appropriate level without using a mean brightness controller during data embedding and provides higher information security compared to the existing RDH approaches.

Single-image dehazing based on dark channel prior and fast weighted guided filtering

Haze scatters light transmitted in the air and reduces image quality, which greatly decreases the interpretability and intelligibility of an image. To solve these problems, we propose an improved real-time image dehazing algorithm based on dark channel prior and fast weighted guided filtering. First, the image is divided into dark areas and bright areas by the K-means clustering algorithm, and the atmospheric light value is calculated according to the proportion of the bright area in the whole image. Second, the fast weighted guided filtering algorithm is employed to generate a refined transmission map, which removes the halo artifact from around the sharp edges. Finally, the gamma correction and automatic contrast enhancement algorithms are used to adjust the brightness and contrast of the dehazing image. Experimental results demonstrate that the proposed method can effectively remove the halo artifacts, improve the color deviation, and retain more details in the images.

Reversible data hiding with automatic contrast enhancement for medical images

Recent developments in data hiding technology have made reversible data hiding (RDH) a popular research topic. This paper is focused on medical images and the up-to-date RDH methods, and proposes an automatic contrast enhancement algorithm called the RDHACEM algorithm considering two metrics: larger embedding capacity and better image visual quality. The proposed algorithm separates medical images into regions of interest (ROI) and regions of non-interest (NROI). It automatically stretches the ROI's grayscale histogram to simultaneously enlarge the embedding capacity of the ROI and enhance the image contrast. Experimental results show that the marked medical images generated by the proposed algorithm have better visual quality and larger embedding capacity in the ROI than those generated by the up-to-date RDH methods.

Towards a computer aided diagnosis (CAD) for brain MRI glioblastomas tumor exploration based on a deep convolutional neuronal networks (D-CNN) architectures

Manuel brain glioblastomas tumor exploration through MRI modalities is time-consuming. It is considered as a harmful and critical task due to highly inhomogeneous tumor regions composition. For this reason, clinicians recommend the Computer-Aided Diagnosis (CAD) tools to ensure a more accurate diagnostic. Based on convolutional Deep-Learning algorithms, this paper investigates a fully automatic CAD for brain Glioblastomas tumors exploration including three steps: pre-processing, segmentation, and finally classification. A denoising and an automatic contrast enhancement method have been applied to preprocess the MRI scans. A Multi-Modal Cascaded U-net architecture, based on Fully Convolutional deep Network (FCN), has been adopted for the Region of Interest (ROI) extraction and finally, Deep Convolutional Neural Network (D-CNN) architecture has been used to classify brain glioblastomas tumor into High-Grade (HG) and Low-Grade (LG). Experiments were performed on the Multimodal Brain Tumor Segmentation Challenge BraTS-2018 datasets benchmark. Several validations metric have been adopted to assess the CAD’s performances. The Dice Metric (DM) parameter has been calculated between the obtained segmentation results and the available ground truth data. The accuracy parameter has been computed for classification performance evaluation. The higher DM and accuracy values could attest the performance and the efficiency of the proposed CAD tool.

Memetic Algorithm Image Enhancement for Preserving Mean Brightness Without Losing Image Features

In the image processing application, contrast enhancement is a major step. Conventional contrast enhancement methods such as Histogram Equalization (HE) do not have satisfactory results on many different low contrast images and they also cannot automatically handle different images. These problems result in specifying parameters manually to produce high contrast images. In this paper, an automatic image contrast enhancement on Memetic algorithm (MA) is proposed. In this study, simple exploiter is proposed to improve the current image contrast. The proposed method accomplishes multi goals of preserving brightness, retaining the shape features of the original histogram and controlling excessive enhancement rate, suiting for applications of consumer electronics. Simulation results shows that in terms of visual assessment, peak signal-to-noise (PSNR) and Absolute Mean Brightness Error (AMBE) the proposed method is better than the literature methods. It improves natural looking images specifically in images with high dynamic range and the output images were applicable for products of consumer electronic.

Improved Adaptive Wavelet Thresholding for Effective Speckle Noise Reduction in Low Contrast Medical Images

Informative images endure from poor contrast and noise during image acquisition. Significant information retrieval necessitates image contrast enhancement and removal of noise as a prerequisite before any further processing can be done. Dominant applications with low contrast images affected by speckle noise are medical ultrasound images. The objective of this work is to improve the effectiveness of the preprocessing stage in medical ultrasound images by enhancing the image while retaining its structural characteristics. For image enhancement, this work proposes to develop an automatic contrast enhancement technique using cumulative histogram equalization and gamma correction based on the image. For noise removal, this work proposes an algorithm Gamma Correction with Exponentially Adaptive Threshold (GCEAT) which suggests the use of GC for contrast enhancement along with a new wavelet-based adaptive soft thresholding technique for noise removal. The proposed GCEAT-based image de-noising is validated with other enhancement and noise removal techniques. Experimental results with low contrast synthetic and actual ultrasound images show that the suggested proposed system performs better than existing contrast enhancement techniques. Encouraging results were obtained with medical ultrasound images in terms of Peak-Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Structural Similarity Index Measure (SSIM) and Average Intensity (AI).

Reversible Data Hiding With Automatic Brightness Preserving Contrast Enhancement

Reversible data hiding with automatic contrast enhancement methods provide an interoperable way to reduce the storage requirement for automatic image enhancement applications: original image can be recovered from the enhanced image without any additional information. Unlike the previous work, where the goal was to maximize the contrast, the proposed method increases the contrast to an appropriate level using an idea called brightness preservation. This is achieved by using an adaptive bin selection process based on the original brightness. Extensive experimental results verify that the enhanced images produced using the proposed method are visually and quantitatively superior than the existing work.

TIR-MS: Thermal Infrared Mean-Shift for Robust Pedestrian Head Tracking in Dynamic Target and Background Variations

Thermal infrared (TIR) pedestrian tracking is one of the major issues in computer vision. Mean-shift is a powerful and versatile non-parametric iterative algorithm for finding local maxima in probability distributions. In existing infrared data, and mean-shift-based tracking is generally based on the brightness feature values. Unfortunately, the brightness is distorted by the target and background variations. This paper proposes a novel pedestrian tracking algorithm, thermal infrared mean-shift (TIR-MS), by introducing radiometric temperature data in mean-shift tracking. The thermal brightness image (eight-bits) was distorted by the automatic contrast enhancement of the scene such as hot objects in the background. On the other hand, the temperature data was unaffected directly by the background change, except for variations by the seasonal effect, which is more stable than the brightness. The experimental results showed that the TIR-MS outperformed the original mean-shift-based brightness when tracking a pedestrian head with successive background variations.

Contrast enhancement for cDNA microarray image based on fourth-order moment

Microarray technology, which can monitor the expression levels of thousands of genes simultaneously, has been widely used in biological experiment. Image processing, as one key step in microarray technology, plays an essential role in microarray analysis. Meanwhile, biological applications require a higher accuracy in each image processing step. However, the low contrast levels of image make it difficult to obtain better processing precision. This paper proposes a fully automatic contrast enhancement (CE) method based on fourth-order moment. Also, a background estimation method is presented to obtain a better microarray image processing result. Comparative results on contrast enhance and gridding revealed that the proposed CE algorithm performs better compared to the adaptive histogram equalization method. Numerous experiments on the Swiss Institute of Bioinformatics (SIB), Joe DeRisi individual (DeRisi), Gene Expression Omnibus (GEO), and Stanford Microarray Database (SMD) data sets also indicate that the proposed CE exerts a tremendous effect on gridding, but has nothing to do with segmentation.

An Efficient Contrast Enhancement Method for Remote Sensing Images

Remote sensing images often suffer low contrast. Although many contrast enhancement methods have been proposed in recent literature, the efficiency and robustness of remote sensing image contrast enhancement is still a challenge. In this letter, a novel self-adaptive histogram compacting transform-based contrast enhancement method for remote sensing images is presented to meet with the requirements of automation, robustness, and efficiency in applications. First, the histogram of an input image is optimized into compact and continuous status with the constraints of the merging cost, the moderate global brightness, and the entropy contribution of gray levels. Then, a local remapping algorithm is proposed to catch more details during the course of gray extending with the linear stretch. Finally, a dual-gamma transform is proposed to enhance the contrast in both bright and black areas. Experimental and comparison results demonstrate that the proposed method yields better results than the state-of-the-art methods and maintains robustness in different cases. It provides an effective approach for remote sensing image automatic contrast enhancement.

Automatic Contrast Enhancement by Transfer Function Modification

In this study, we propose an automatic contrast enhancement method based on transfer function modification (TFM) by histogram equalization. Previous...

Automatic contrast enhancement of brain MR images using Average Intensity Replacement based on Adaptive Histogram Equalization (AIR-AHE)

Medical imaging is the most established technique of visualizing the interior of the human body without the risk of the non-invasive effect. This technology is designed to produce images, and it is also capable of representing information about the screening location. In MRI imaging, the poor image quality particularly the low contrast image may provide insufficient data for the visual interpretation of such affected locations. Therefore, the need of image enhancement arises to improve image visions and also to computationally support the image processing technique. In general, conventional contrast enhancement methods may work well for some images. However, in MRI brain image, there are often more complex situations where the WMH signal is high but it may mistakenly be considered as other brain tissues such as CSF. With the motivation to classify the most possible WMH regions, this paper proposes a novel image contrast algorithm of WMH enhancement for MRI image. This algorithm is also known as the Average Intensity Replacement – Adaptive Histogram Equalization (AIR-AHE). The proposed algorithm is applied to the FLAIR image based on the intensity adjustment and contrast mapping techniques. The proposed algorithm for the image enhancement is superior to the existing methods by using image evaluation quantitative methods of PSNR, average gradient values and MSE. Furthermore, the edge information pertaining to the potential WMH regions can effectively increase the accuracy of the results.

Automatic Image Contrast Enhancement for Small Ship Detection and Inspection Using RADARSAT-2 Synthetic Aperture Radar Data

This study devises an automatic synthetic aperture radar (SAR) image enhancement method for ship detection and inspection for installation in a near-real-time automatic high-speed processing system. The proposed method was examined in small ship inspection and detection over the Ieodo Ocean area off Korea using RADARSAR-2 HV-polarization data. The proposed method involves four steps. First, the SAR input data is converted into a highly compressed gray scale image, which enables both computer screen display and high-speed processing due to its light volume. Second, the overall contrast is adjusted by power-law scaling to strengthen the target discrimination, which is attenuated because of the inefficiency of one-sided intensity distribution. This additionally provides excellent target visibility. Third, the intensity of the area in which targets and clutter coexist is rescaled from 0 to 255 using min-max linear stretching. This suppresses background clutter and makes targets more easily distinguishable from the clutter. Lastly, the remaining clutter is successfully eliminated using a median filter. As a result, an output image is obtained that is close to binary data and enables ship detection using only simple global thresholding. Our ship detection results were compared with that of ships identified using an automatic identification system and those visible in high-precision images by visual inspection. We verified that our method offers a high detection rate for small ships and does not involve complexity in distribution assumption, filtering or thresholding. The potential of our method is confirmed as an automatic SAR enhancement method for near-real-time ship detection and inspection.