histogram equalization algorithm

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Image Contrast Enhancement using Tri-histogram Equalization based on Minimum and Maximum Intensity Occurrence
                 

Image Contrast Enhancement using Tri-histogram Equalization based on Minimum and Maximum Intensity Occurrence  

Although various techniques are available to solve a broad range of enhancement problems, very little effort has been made on tri-histogram based contrast enhancement. In this paper, we present a new tri- histogram equalization algorithm refers to Minimum and Maximum Intensity based Tri-Histogram Equalization (MMITHE). Study shows that, the proposed method performs better in brightness preservation and contrast enhancement for low contrast and under-exposed images. The rest of the paper is organized as follows: Section 2 presents the new tri-histogram based separation technique using minimum and maximum intensity as division points. Section 3 describes about absolute mean brightness error measurement feature used to measure enhanced images brightness preservation and quality. Section 4 gives experimental results with section 5 concluding the paper.
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Dorsal Hand Vein Enhancement and Fake Vein Detection

Dorsal Hand Vein Enhancement and Fake Vein Detection

Abstract. In order to extract better vein feathers, image preprocessing is necessary. So this paper propose a new approach to enhance images. The enhancement algorithm uses guided filter (GF) to process hand vein images. The guided filter is used as an edge-preserving smoothing operator. The guided filter enhancement algorithm is effective comparing with bilateral filter (BF), histogram equalization (HE), adaptive histogram equalization algorithm (AHE) and contrast limited adaptive histogram equalization (CLAHE). We use several methods to enhance dorsal hand vein images, the recognition rate with guided filter is the best. For the security, a fake vein detection algorithm is used to discriminate the real vein and fake vein images.
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ENHANCING IMAGE CONTRAST OF MAMMOGRAM & EQUALIZATION OF HISTOGRAMS

ENHANCING IMAGE CONTRAST OF MAMMOGRAM & EQUALIZATION OF HISTOGRAMS

This paper, a new contrast enhancement algorithm referred as the enhancing contrast and Histogram equalization algorithm .Equalization with better brightness preservation is proposed. The algorithm is a novel extension of HE. The main idea lies on separating the histogram using the threshold level that would yield minimum Absolute Mean Brightness level. The ultimate goal behind the algorithm is to allows maximum level of brightness preservation in Bi-Histogram Equalization to avoid unpleasant artifacts and unnatural enhancement due to excessive equalization while enhancing the contrast of a given image as much as possible. We have done Simulation results using sample image which represent images with very low, very high and medium mean brightness have demonstrated that the cases which are not handled well by HE.
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Image Contrast Enhancement using Learning Vector Quantization

Image Contrast Enhancement using Learning Vector Quantization

They presented an advanced Histogram Equalization algorithm for contrast Enhancement .Global Histogram Equalization is simple and fast but its contrast enhancement power is relatively low. Local histogram enhancement is on the other hand, can enhance overall contrast more effectively. For High contrast and simple calculation a low pass filter type mask is proposed. This mask also eliminates the blocking effect of non-overlapped sub-block histogram-equalization. The low pass filter type mask is realized by partially overlapped sub-block histogram equalization (POSHE)[17].POSHE is derived from local histogram equalization but it is much effective and much faster. In order to make the histogram equalization locally adaptive for higher contrast, and reduce the computation complexity ,non- overlapped sub-block histogram equalization is essential. In this method, all pixels in each sub-block are histogram equalized using the sub-block’s histogram . Further, these sub-blocks are not overlapped with adjacent sub-blocks, so the computation complexity is reduced considerably. This non- overlapped method cannot avoid a blocking effect. To eliminate these effects, additional operations are needed. Blocking effects occur due to shape differences between histogram-equalization functions of neighboring sub-blocks. The most important feature of POSHE is Low-pass filter shaped mask.
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Fuzzy Techniques for Enhancement of Low Contrast Images

Fuzzy Techniques for Enhancement of Low Contrast Images

Fuzzy technique plays an important role in image segmentation, processing and contrast enhancement. The commonly used techniques for contrast enhancement fall into two categories: (1) indirect methods and (2) direct methods. Direct method of contrast enhancement is more useful because it is considering both global and local information of the image. Fuzzy logic has been found many applications in image processing and pattern recognition, image segmentation etc. With this above information, we propose a novel adaptive direct fuzzy contrast enhancement method based on the fuzzy entropy and fuzzy set theory. The experimental results have demonstrated that the proposed algorithm is more adaptive and effective for contrast enhancement compared to other method. Moreover, it significantly reduces the over enhancement/under-enhancement due to its better adaptive capability.
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Enhancement of Medical Images using Histogram Based Hybrid Technique

Enhancement of Medical Images using Histogram Based Hybrid Technique

values known as Histogram Equalization. In this paper, author evaluated the performance of different Histogram Equalization techniques for gray scale static images. In order to evaluate, the performance of these techniques, are examined on the basis of AMBE, PSNR and Entropy metrics. In this process enhancement techniques are applied on the images with different sizes and received from different application fields like real images, medical images etc. It is well illustrated that Brightness Preserving Dynamic Histogram Equalization (BPDHE) is the most suitable technique in terms of mean brightness preservation as it has least average AMBE value. In terms of PSNR, MPHEBP is the most suitable technique because it has the highest average PSNR value. In terms of Entropy, BBHE and RSIHE(r=2) are the best techniques, since these have the highest average Entropy values. The performance of BPDHE is not satisfactory in terms of Entropy. Swati Khidse (2013), here
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Title: A Comparison of Thresholding Based Image Enhancement Techniques

Title: A Comparison of Thresholding Based Image Enhancement Techniques

Abstract - Image Enhancement is a vast area of Image Processing with its applications in different areas. Image Enhancement is used to transform digital images to enrich the visual information inside it. It is an initial operation for almost all vision and image processing assignment in several areas such as computer vision, biomedical image Processing, forensic image analysis, remote sensing and fault detection. In image enhancement certain transformations are applied upon an input image to obtain a visually more acceptable, more comprehensive and less noisy output image. In this paper four histogram equalization based Image Enhancement Techniques, CHE (Conventional Histogram Equalization), BBHE (Brightness Preserving Bi-Histogram Equalization), DSIHE (Dual Sub-Image Histogram Equalization) and MMBEBHE (Minimum Mean Brightness Error Bi-HE), are compared. All these techniques are based on partitioning of histogram of image and then equalizing each part separately. These techniques are assessed qualitatively and after examining output image visually, we see if it retains an appearance which is perfectly natural.
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Survey on Image Enhancement

Survey on Image Enhancement

In this method a low pass filter type mask is used to get a nonoverlapped sub block histogram equalization function to produce the high contrast associated with local histogram equalization. The low pass filter type mask is realized by partially overlapped sub block histogram equalization(POSHE).Since with the proposed method, the sub block are much less overlapped the computation overhead is reduced by a factor of about 100 compare to that of local histogram equalization. The procedures of Poshe are given below:

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SIGNIFICANT PREPROCESSING METHOD IN EEG BASED EMOTIONS CLASSIFICATION

SIGNIFICANT PREPROCESSING METHOD IN EEG BASED EMOTIONS CLASSIFICATION

Histogram equalization (Heq) is a new normalization operation of speech features. It has been developed firstly to treat the digital image by normalizing the extracted visual features such as grey-level and contrast [20]. The Heq has been successfully employed in many features extraction approach to improve the performance and robustness of ASR applications [14][15][16][17]. It can be defined as a transformation of probability density function (pdf) of speech vectors (or testing) to a reference pdf (or training), i.e., this transformation equalizes the histogram by converting speech vectors histogram to reference histogram [21]. The Heq can be formulated and described as follows [22]: Let z is the features set with probability density function (PDF) denoted by P(z) and cumulative density function (CDF) denoted by C ( ) z z . This set is transformed by a
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AN EFFICIENT TECHNIQUE FOR ENHANCING MRI BRAIN IMAGES

AN EFFICIENT TECHNIQUE FOR ENHANCING MRI BRAIN IMAGES

To build up the picture include for human discernment. It is characterized as a strategy for a picture handling to such an extent that the outcome is a great deal more suitable than the first picture. Histogram balance is a major instrument in picture Enhancement. It is probably going to help in discerning of how the normal power level in the histogram of the evened out picture is superior to the first. Its capacity is increment the dynamic scope of power levels in a picture [3].

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Enhance Security for Medical Images through Secure Force Cryptography with Steganography Techniques

Enhance Security for Medical Images through Secure Force Cryptography with Steganography Techniques

communication).Steganography is totally completely different from cryptography at intervals the sense that cryptography focuses entirely on keeping the contents of a message secret, whereas steganography focuses on keeping the existence of a message secret. Image steganography in which the information is hidden exclusively in images uses LSB algorithm. The two necessary conditions for the steganography security area unit obtained. Under the present technology state of affairs, analyze the identicalness of the cover and stego-cover, and think about that the steganography security ought to trust the key secrecy with algorithms open [11].The higher level security one has the higher level attacks one can resist.By specifying the role of key in steganography, the reqired conditions for a secure steganography algorithm rule in theory are formally conferred Image steganography terminologies are as follows:-
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Histogram Equalization Based Image Enhancement Techniques For Brightness Preservation And Contrast Enhancement Sonam, II Rajiv Dahiya M.Tech Scholar, Dept. of ECE ,P.D.M College of Engineering, Bahadurgarh, India

Histogram Equalization Based Image Enhancement Techniques For Brightness Preservation And Contrast Enhancement Sonam, II Rajiv Dahiya M.Tech Scholar, Dept. of ECE ,P.D.M College of Engineering, Bahadurgarh, India

Image enhancement is aprocess of changing the pixels intensity of the input image;to make the output image subjectively look better. Contrast enhancement is an important area in image processing for both human and computer vision. It is widely used for medical image processing and as a pre-processing step in speech recognition, texture synthesis, and many other image/video processing applications. Contrast enhancement plays a crucial role in image processing applications, such as digital photography, medical image analysis, remote sensing, LCD display processing, and scientific visualization. There are several reasons for an image/ video to have poor contrast: the poor quality of the used imaging device, lack of expertise of the operator and the adverse external conditions at the time of acquisition. These effects result in under-utilization of the offered dynamic range. As a result, such images and videos may not reveal all the details in the captured scene and may have a washed out and unnatural look. Contrast enhancement targets to eliminate these problems, thereby to obtain a mor visually-pleasing or informative image or both. Histogram equalization is a well-known contrast enhancement technique due to its performance on almost all types of image. Contrast is created by the difference in luminance reflectance from two adjacent surfaces. In our visual perception, contrast is determined by the difference in the color and brightness of an object with other objects. If the contrast of an image is highly concentrated on a specific range, the information may be lost in those areas which are excessively and uniformly concentrated. The problem is to enhance the contrast of an image in order to represent all the information in the input image.Brightness preserving methods are in very high demand to the consumer electronic products. Numerous histogram equalization (HE) based brightness preserving methods tend to produce unwanted artefacts.
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A Method of Medical Image Enhancement using wavelet Based Fusion and Analysis

A Method of Medical Image Enhancement using wavelet Based Fusion and Analysis

Medical image processing is a exigent field of research since the encapsulating images suffers from the low and deficient contrast. The efficiency of the medical image processing depends on the quality of the encapsulate medical images. Major factors for the low contrast medical images are age of encapsulating equipments, inferior illumination conditions and in-experience of medical staff. Thus, contrast enhancement methods are used for ameliorating the contrast of medical images before being used. In this paper an amalgam of the contrast limited adaptive histogram equalization (CLAHE) method and the wavelet based Fusion techniques are used for designing the efficient medical image enhancement method. Method is capable of adapting the Fusion rules adaptively for best enhanced results. First CLAHE image enhanced is used for ameliorating the contrast of the medical images. Then in second stage 2D discrete wavelet transformation based adaptive image fusion is used for fusing the original and CLAHE output images. For testing the execution of SNR and entropy are calculated and used as parameters. It is found that based on adaptive Fusion the visual content of the medical images are efficiently enhanced under all kind of encapsulating environments.
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An Enhancement of Images Using Recursive
          Adaptive Gamma Correction

An Enhancement of Images Using Recursive Adaptive Gamma Correction

Cheng H. D, et.al, (2004), in this Image enhancement is one of the most important issues in low-level image processing. Primarily, enhancement methods can be classified into two modules: global and local methods. In this author said that the multi-peak generalized histogram equalization (multi-peak GHE) is proposed. The global histogram equalization is improved by using multi-peak histogram equalization combined with local information. Our observation result, demonstrate that the proposed method can enhance the images effectively. Image enhancement is one of the most important issues in low- level image processing. All the methods are based either on local information or on global information. A novel approach using both local and global information to enhance image is studied in this pa- per. This method adopts the traits of existing methods. It also makes the degree of the enhancement completely controllable. Experimental results show that it is very effective in enhancing images with low contrast, apart from of their brightness. Multi-peak GHE technique is very effective to enhance various kinds of images when the proper features (local information) can be extracted [7].
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A NOVEL CONTRAST ENHANCEMENT METHOD BY ARBITRARILY SHAPED WAVELET TRANSFORM THROUGH HISTOGRAM EQUALIZATION

A NOVEL CONTRAST ENHANCEMENT METHOD BY ARBITRARILY SHAPED WAVELET TRANSFORM THROUGH HISTOGRAM EQUALIZATION

Contrast enhancement is one of the useful methods to enhance the features of the images in digital image processing. It is achieved by improving the entire brightness range in the given image. Currently, enhancing the image using this method has a limitation. It stretches the histogram of gray levels. This may occur level changes after applying the image contrasts. That is, the adjacent pixels have a large range of difference, so wavelet coefficients are very large and the level change is large. When the image contrasts are enhanced by this method, an extreme level change may be occurred in the image. This level change grows in the vicinity of the edge .The pixel value contained in the edge changes extremely. Due to this extreme change, the characteristic of the image is lost. That is, the pixel values are scaled-out. Therefore, specialized algorithms have been tried to solve image enhancement problem.
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1.
													Adaptive histogram equalization technique for enhancement of coloured image quality

1. Adaptive histogram equalization technique for enhancement of coloured image quality

Huang et al. [6] proposed a work of fiction hardware oriented contrast enhancement algorithm that will be frequently actualized viablely for equipment outline. The proposed h/woriented contrast upgrade calculation accomplishes great picture quality by measuring the results of subjective and quantitative analyses. To diminish equipment cost and enhance equipment use for realtime execution, a decrease in circuit zone is proposed through use of parameter controlled reconfigurable engineering. The investigation outcome demonstrated that the proposed hardware oriented contrast improvement calculation gives the run of the mill outline rate of 48.23 casings/s at hd determination 1920 × 1080.
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A Novel Dark Channel Dehazing Algorithm Based on Adaptive Filter Enhanced SSR Theory

A Novel Dark Channel Dehazing Algorithm Based on Adaptive Filter Enhanced SSR Theory

Low visibility in foggy days results in less contrasted and blurred images with color distortion which adversely affects and leads to the sub-optimal perfor- mances in image and video monitoring systems. The causes of foggy image degradation were explained in detail and the approaches of image enhance- ment and image restoration for defogging were introduced. The study pro- posed an enhanced and advanced form of the improved Retinex theory-based dehazing algorithm. The proposed algorithm achieved novel in the manner in which the dark channel prior was efficiently combined with the dark-channel prior into a single dehazing framework. The proposed approach performed the first stage in dehazing within the dark channel domain through imple- mentation with an adaptive filter. This novel approach allowed for the dark channel features to be efficiently refined and boosted, a scheme, which ac- cording to the obtained results, significantly improved dehazing results in lat- er stages. Experimental results showed that this approach did little to trade-off dehazing speed for efficiency. This makes the proposed algorithm a strong candidate for real-time systems due to its capability to realize efficient dehaz- ing at considerably rapid speeds. Finally, experimental results were provided to validate the superior performance and efficiency of the proposed dehazing algorithm.
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Surveying on Fruit Disease Recognition Using Machine Learning

Surveying on Fruit Disease Recognition Using Machine Learning

In this paper, the Feature extraction is an significant constituent of the pattern recognition system. It carries out the two assignments: converting input parameter vector into the feature vector and or reducing its dimensionality. The distinct feature extraction algorithm makes a classification process more effectual and efficient. The allocation and recognition of the cotton leaf diseases are of the major importance as they have a cogent and the momentous impact on the quality and production of the cotton . In this work, they present the snake based approach for the segmentation of images of the diseased cotton leaves. They extract Hu’s moments which can be used as the shape descriptors for the classification. A theory of the two-dimensional moment invariants for the planar geometric figures is also presented. Three diseases have been considered, namely the Bacterial Blight,
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A ROBUST COLOR IMAGE WATERMARKING TECHNIQUES WITH CONTRAST ENHANCEMENT

A ROBUST COLOR IMAGE WATERMARKING TECHNIQUES WITH CONTRAST ENHANCEMENT

ABSTRACT: In recent years, many works on digital image watermarking have been proposed all aiming at protection of the copyright of an image document or authentication of data. With the help of my proposed Modified LSB watermarking embedding with Color Histogram Equalization -Contrast Adjustment (CHE-CA) algorithm, the high contrast watermarked image is obtained & watermark can easily be extracted in both clean and noisy environments. Experiments are performed to verify the robustness of the proposed algorithm. The results show that the proposed algorithm is superior to other algorithm in terms of providing a high PSNR. It is also shown that the proposed algorithm is highly robust against various kinds of attacks such as compression, noise, filtering, cropping & rotation.
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Histogram Equalization Tool: Brightness Preservation and Contrast Enhancement using Segmentation with Opening by Reconstruction

Histogram Equalization Tool: Brightness Preservation and Contrast Enhancement using Segmentation with Opening by Reconstruction

In general, histogram equalization contrasts the differentiation of the low histogram regions and grows the difference of the high histogram areas [14]. Accordingly, when the object of enthusiasm for a picture just involves a pinch of the picture, this article won't be prosperously upgraded by histogram balance and this system also colossally pushes the intensities towards the privilege or the left half of the histogram, creating level immersion impacts. To surmount these issues, Clipped Histogram Equalization (CHE) systems are used to confine the improvement rate. CHE reshape the data histogram by decreasing or increasing the worth in the histogram's canisters predicated on an edge limit up to the adjustment is occurring. This edge obligation is withal kenned as far as possible, or the level of the histogram. The histogram will be cut predicated on this edge esteem. Now and again cut part will be redistributed once more to the histogram and afterward histogram leveling is completed. Cut Histogram Equalization (CHE) is much a bigger number of more effectual for complexity upgrade than the subsisting HE-predicated
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