Weighted Histogram Equalization Research Articles

Colour image enhancement using weighted histogram equalization with improved monarch butterfly optimization

ABSTRACT Image enhancement is a technique for improving the quality of an image so that it can be viewed by both humans and machines. The primary purpose of picture contrast enhancement is to improve the image’s visual quality. Histogram equalisation (HE) is one way to increase contrast. One disadvantage of He is that it does not sustain brightness while increasing contrast since a sdden mean shift occurs during the equalisation process. A new image enhancement method is established with weighted histogram equalisation with Oppositional-based Customised Monarch Butterfly Optimisation (OCMBO), for better visual perception and improving image quality. The collected input image is in RGB format. This image is then converted into YCbCr format for contrast stretching. In digital image processing, the YCbCr colour space is often used to take advantage of the lower resolution capability of the human visual system for colour concerning luminosity. The weighted histogram equalisation with Oppositional-based Customised Monarch Butterfly Optimisation (OCMBO) is applied to the converted image’s Y component, and then the Cr, Cb and modified Y components are combined back into RGB format.

Infrared image contrast enhancement using adaptive histogram correction framework

To enhance infrared images' contrast and visual effect, this paper proposes a contrast enhancement method based on adaptive histogram correction and equalization. Unlike the previous histogram equalization method, this method combines adaptive histogram correction and histogram equalization to effectively reduce artifacts and insufficient local detail enhancement caused by traditional methods. In our method, a more uniform histogram is first obtained by recursive separation weighted histogram equalization, then a weighted averaging method is used to establish a connection between this histogram and the original histogram to correct the histogram, and finally, the corrected histogram is used to establish a mapping relationship between the input gray levels and the output gray levels. The experimental results show that the infrared image contrast enhancement using this method is better in terms of detail representation in dark areas, overall image brightness maintenance, contrast enhancement, and suppression of artifacts, and is better than or similar to existing methods.

A novel detail weighted histogram equalization method for brightness preserving image enhancement based on partial statistic and global mapping model

Histogram equalization (HE) is a classic and widely used image contrast enhancement algorithm for its good performance and high efficiency. However, over-enhancement caused by high peak in the histogram affects the subjective quality of the image processed by HE to a large extent. In this paper, a detail weighted histogram equalization (DWHE) method is proposed based on a novel histogram modification (HM) model named partial statistic and global mapping (PSGM) to alleviate high peak and suppress over-enhancement. Moreover, the authors implement a refined version of gamma correction (GC) named texture enhancement function (TEF) on high-frequency images to reduce the noise amplification effect. At last, the authors propose a novel adaptively weighted pixel-level image fusion method to further reduce the phenomenon of over-enhancement and improve brightness distribution. Both subjective and quantitative evaluations are conducted on images containing a variety of scenes. Compared with several state-of-the-art image enhancement methods, the proposed framework obtained generally the best performance in aspects of both subjective appearance and objective evaluation indices. Therefore, it is proved that the proposed methods can effectively alleviate the over-enhancement, enrich image details, and efficiently improve the visual quality while preserving the brightness of the image.

Image contrast enhancement algorithm for X-ray observation of space materials in situ

Chinese Space Station has planned a high-temperature material science experiment rack, equipped with an X-ray projection imaging module, to support the development of material experiments and research in space. BiFeO3 has been selected as the first batch of experimental materials for Chinese Space Station. The melting and solidification process of BiFeO3, an opaque, high-temperature material, is observed by X-ray observation module in situ. X-ray is the dominant way to observe opaque materials due to its penetrability. In-situ observation of materials is the top priority of this study, so we have strict requirements on image quality, and high-quality images can better analyze the properties and properties of materials. Limited by narrow size and high temperature conditions, the X-ray images collected have low contrast, serious noise pollution, and poor imaging quality. To enhance the contrast and improve the edge details of such images, a grayscale weighted histogram equalization combined with high-frequency enhancement (GWHE-HFE) algorithm is proposed. First, we add a mask to the input image to obtain the region of interest (ROI), and then filter out the low-frequency components of the image by Gaussian high-pass filter to preserve high-frequency details. Second, the image obtained in the previous and the X-ray image of ROI are respectively multiplied by a coefficient and added to obtain the edge-emphasized X-ray image. And then, we use grayscale weighted histogram equalization (GWHE) to process the image obtained in the second step to obtain the contrast enhanced X-ray image. The enhanced image shows the crystal grains and the thin bands where the solid and the melt intersect, and it is helpful to accurately locate the solid solution interface. Experiments on X-ray images of BiFeO3 growth demonstrate that this combined method outperforms existing ones both qualitatively and quantitatively, providing an in-depth and effective analysis method for in high-temperature material-science experiments.

RDH-based dynamic weighted histogram equalization using for secure transmission and cancer prediction

Image contrast enhancement is a prerequisite and plays a very important role in many image processing field like medical imaging, face recognition, computer-vision, and satellite imaging. In this paper we proposed reversible data hiding based Limited Dynamic Weighted Histogram Equalization techniques for Abnormal Tumor regions which improve the contrast, transmit the hidden secret information, preserve its brightness intensity and original appearance of the image. We have implemented Otsu’s method to segment the input image into two sub-histogram regions of interest (ROI) and non-region of interest; furthermore, the sub-histograms ROI region equalized independently without of over-enhancement and any loss of hidden and diagnostic data. Our proposed method is more efficient to precisely preserve the brightness of the image and extract the secret information with contrast image reversibly; besides, different classifiers are used to classify the brain cancer to check the performance of our proposed method.

An Efficient Algorithm to Highlight Details in Infrared and Visible Image Fusion

To improve the fusion quality of infrared and visible images and highlight target and scene details, in this paper, a novel infrared and visible image fusion algorithm is proposed. First, a method for combining dynamic range compression and contrast restoration based on a guided filter is adopted to enhance the contrast of visible source images. Second, guided filter-based image multiscale decomposition is used to decompose images into base layers and detail layers. For base layer fusion, a fusion strategy based on the detail and energy measurements of the source image is proposed to determine the pixel value of the fused image base layer such that the energy loss of the fusion can be reduced and the texture detail features are highlighted to obtain more source image details. Finally, recursive separation and weighted histogram equalization methods are applied to optimize the fused image. Experimental results show that the fusion algorithm and fusion strategy proposed in this paper can effectively improve fusion image clarity, while more detailed target and scene information can still be retained.

WITHDRAWN: Low light image contrast enhancement using advanced perfusion technique

Abstract The images captured tend to degrade due to the scattering and absorption of light. This may be due to the diminished colors, low light and undistinguishable objects in the image. For the improvement of quality of such degraded images, Advanced Perfusion technique is employed to enhance the contrast of the low light Image. In this technique, the input image in RGB color space is converted to LAB color spaces. Two versions of input image are considered, in which, the first image is processed for brightness contrast enhancement and the second image is processed for color contrast enhancement. For the enhancement of color contrast, the second image is passed through a Recursive Separated and Weighted Histogram Equalization, (RSWHE) followed by power law transformation. Two main features are extracted from both the images, namely, Laplacian weight map and Saliency weight map. These two features of the two LAB images are fused together to produce a final enhanced image, which has improved brightness contrast and color contrast. The proposed image enhancement technique is evaluated with various performance parameters such as Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR), and Lightness Order Error (LOE). The performance of the proposed method is then compared with the existing methods for validation.

Contrast Enhancement Using Optimum Threshold Selection

In the recent era, a boom was observed in the field of information retrieval from images. Digital images with high contrast are sources of abundant information. The gathered information is useful in the precise detection of an object, event, or anomaly captured in an image scene. Existing systems do uniform distribution of intensities and apply intensity histogram equalization. These improve the characteristics of an image in terms of visual appearance. The problem of over enhancement and the increase in noise level produces undesirable visual artefacts. The use of Otsu's single threshold method in existing systems is inefficient for segmenting an image with multiple objects and complex background. Additionally, these are incapable to improve the yield of the maximum entropy and brightness preservation. The aforementioned limitations motivate us to propose an efficient statistical pipelined approach, the Range Limited Double Threshold Weighted Histogram Equalization (RLDTWHE). This approach is an integration of Otsu's double threshold, dynamic range stretching, weighted distribution, adaptive gamma correction, and homomorphic filtering. It provides optimum contrast enhancement by selecting the best appropriate threshold value for image segmentation. The proposed approach is efficient in the enhancement of low contrast medical MRI images and digital natural scene images. It effectively preserves all essential information recorded in an image. Experimental results prove its efficacy in terms of maximum entropy preservation, brightness preservation, contrast enhancement, and retaining the natural appearance of an image.

Particle swarm optimization-based local entropy weighted histogram equalization for infrared image enhancement

Infrared image enhancement plays a significant role in intelligent urban surveillance systems for smart city applications. Unlike existing methods only exaggerating the global contrast, we propose a particle swam optimization-based local entropy weighted histogram equalization which involves the enhancement of both local details and fore-and background contrast. First of all, a novel local entropy weighted histogram depicting the distribution of detail information is calculated based on a modified hyperbolic tangent function. Then, the histogram is divided into two parts via a threshold maximizing the inter-class variance in order to improve the contrasts of foreground and background, respectively. To avoid over-enhancement and noise amplification, double plateau thresholds of the presented histogram are formulated by means of particle swarm optimization algorithm. Lastly, each sub-image is equalized independently according to the constrained sub-local entropy weighted histogram. Comparative experiments implemented on real infrared images prove that our algorithm outperforms other state-of-the-art methods in terms of both visual and quantized evaluations.

Medical Image Contrast Enhancement using Range Limited Weighted Histogram Equalization

Contrast enhancement and brightness preservation are the crucial steps for image quality control for developing informative and visually pleasing images. Histogram equalization based image enhancement tool does not provide the brightness preservation and better contrast enhancement. This may cause loss in diagnostic information in case of medical images. An attempt has been made to integrate range limited and weighted histogram equalization with adaptive gamma correction followed by homomorphic filtering to study the improvement in contrast as well as to preserve the essential details of the image. An image segmentation based on an efficient Otsu’s method has been implemented. The experimental results obtained are found to be optimal for generating enhanced images according to both quantitative estimation and qualitative human visual inspection. An excellent performance in terms maximum entropy preservation, better contrast enhancement and the best visual appearance of low contrast medical images has been achieved.

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

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

The Role of Image Enhancement in Citrus Canker Disease Detection

Digital image processing is employed in numerous areas of biology to identify and analyse problems. This approach aims to use image processing techniques for citrus canker disease detection through leaf inspection. Citrus canker is a severe bacterium-based citrus plant disease. The symptoms of citrus canker disease typically occur in the leaves, branches, fruits and thorns. The leaf images show the health status of the plant and facilitate the observation and detection of the disease level at an early stage. The leaf image analysis is an essential step for the detection of numerous plant diseases. The proposed approach consists of two stages to improve the clarity and quality of leaf images. The primary stage uses Recursively Separated Weighted Histogram Equalization (RSWHE), which improves the contrast level. The second stage removes the unwanted noise using a Median filter. This proposed approach uses these methods to improve the clarity of the images and implements these methods in lemon citrus canker disease detection.

Recursive weighted multi-plateau histogram equalization for image enhancement

Histogram equalization is broadly used for contrast enhancement. However, it does not take brightness preservation into account. It is not suitable for real time applications like medical, synthetics aperture radar (SAR), consumer electronic products, etc. whereas the brightness preservation is of vital importance to avoid the annoying artifacts. This paper proposes a histogram equalization based method called recursive weighted multi-plateau histogram equalization (RWMPHE). The essential idea is to segment the histogram into two or more sub histograms followed by clipping with six plateau limits. Weighting process using normalized power law modifies the clipped histogram to equalize each partition independently using histogram equalization. Recursive mean separate histogram equalization (RMSHE), recursive sub-image histogram equalization (RSIHE) and recursively separated and weighted histogram equalization (RSWHE) are methods that perform similar histogram segmentation but do not carry out clipping with weighting for histogram modification. In this paper, it is shown that RWMPHE preserve the brightness and enhances the contrast more precisely and accurately than existing methods.

Intensity Weighted Histogram Equalization Method for Night Vision

This paper explores the possibility of utilizing histogram equalization on images captured in poor lighting conditions in order to expand their histogram dynamic range, enhancing their contrast and effectively provide night vision for such images. Then, some drawbacks of standard histogram equalization for dark images, caused mainly due to the clustering of pixels around the lowest intensities are exposed, and Enhanced Intensity Weighted Histogram Equalization is presented as a solution to obtain more realistic night vision images by incorporating the normalized weight of each pixel intensity into the calculations and spreading the histogram values to fill in the gaps, reducing noisy high frequency changes. This technology can be applied to new capture devices that detect the lack of illumination and engage Enhanced Intensity Weighted Histogram Equalization to provide low light capture, useful for surveillance, driving, medical imaging, and even space exploration.

이미지 화질개선을 위한 Weber-Fechner 법칙을 적용한 가중 히스토그램 균등화 기법

저화질 이미지의 화질 개선에는 전통적으로 히스토그램균등화 기법이 사용되어 왔다. 히스토그램균등화 기법은 입력 이미지의 누적밀도함수를 변환함수로 사용하는 기법으로 이는 이론상 최대의 엔트로피를 가지지만 주관적 화질 측면에서는 백화현상이 나타나는 문제점이 있다. 본 논문에서는 히스토그램균등화 기법 기반의 가중 히스토그램 균등화 기법을 제안한다. 이는 인간의 시각특성을 반영한 Weber-Fechner 법칙을 사용하며 입력영상에 독립적인 변환함수를 제공하는 여러 이미지 화질 개선 기법들이 가지는 문제점을 해결하기 위해서 동적영역 재조정 과정을 포함한다. 최종적으로 재조정된 동적영역 범위 내에서 Weber-Fechner 법칙을 적용한 변환함수와 히스토그램균등화 기법을 통해 얻어진 변환함수간의 가중 평균을 통하여 변환함수를 생성한다. 실험결과 제안하는 알고리즘은 주관적 화질 측면에서 대비비를 효과적으로 향상시키는 것을 보여주며, 엔트로피 또한 비교에 사용된 여러 이전의 방법들과 비교하여 유사하거나 높은 값을 가지는 것을 볼 수 있었다. A histogram equalization method have been used traditionally for the image enhancement of low quality images. This uses the transformation function, which is a cumulative density function of an input image, and it has mathematically maximum entropy. This method, however, may yield whitening artifacts. This paper proposes the weighted histogram equalization method based on histogram equalization. It has Weber-Fechner's law for a human's vision characteristics, and a dynamic range modification to solve the problem of some methods, which yield a transformation function, regardless of the input image. Finally, the proposed transformation function was calculated using the weighted average of Weber-Fechner and the histogram equalization transformation functions in a modified dynamic range. The simulation results showed that the proposed algorithm effectively enhances the contrast in terms of the subjective quality. In addition, the proposed method has similar or higher entropy than the other conventional approaches.

Recursively separated and weighted histogram equalization for brightness preservation and contrast enhancement

This paper proposes a new histogram equalization method, called RSWHE (recursively separated and weighted histogram equalization), for brightness preservation and image contrast enhancement. The essential idea of RSWHE is to segment an input histogram into two or more sub-histograms recursively, to modify the sub-histograms by means of a weighting process based on a normalized power law function, and to perform histogram equalization on the weighted sub-histograms independently. RSIHE (recursive sub-image histogram equalization) and RMSHE (recursive mean separate histogram equalization) are some methods similar to RSWHE, but they do not carry out the above weighting process. We show that compared to other existent methods, RSWHE preserves the image brightness more accurately and produces images with better contrast enhancement.