Showing papers on "Histogram equalization published in 2008"

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

Abstract: 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.

Parallel Image Processing Based on CUDA

Abstract: CUDA (compute unified device architecture) is a novel technology of general-purpose computing on the GPU, which makes users develop general GPU (graphics processing unit) programs easily. This paper analyzes the distinct features of CUDA GPU, summarizes the general program mode of CUDA. Furthermore, we implement several classical image processing algorithms by CUDA, such as histogram equalization, removing clouds, edge detection and DCT encode and decode etc., especially introduce the first two algorithms. If we donpsilat take the data transfer time in experiment between host memory and device memory into account, as the image size increase, histogram computation can get a more than 40x speedup, removing clouds can get an about 79x speedup, DCT can gain around 8x and edge detection more than 200x.

Fast image segmentation based on K-Means clustering with histograms in HSV color space

Abstract: A fast and efficient approach for color image segmentation is proposed. In this work, a new quantization technique for HSV color space is implemented to generate a color histogram and a gray histogram for K-Means clustering, which operates across different dimensions in HSV color space. Compared with the traditional K-Means clustering, the initialization of centroids and the number of cluster are automatically estimated in the proposed method. In addition, a filter for post-processing is introduced to effectively eliminate small spatial regions. Experiments show that the proposed segmentation algorithm achieves high computational speed, and salient regions of images can be effectively extracted. Moreover, the segmentation results are close to human perceptions.

Adaptive contrast enhancement using gain-controllable clipped histogram equalization

Abstract: Histogram equalization is a simple and effective method for contrast enhancement as it can automatically define the intensity transformation function based on statistical characteristics of the image. However, it tends to alter the brightness of the entire image, which it is not suitable for consumer electronic products, where preservation of the original brightness is essential to avoid annoying artifacts. This paper presents a new contrast enhancement method for generalization of the existing bihistogram equalization (BHE) and recursive mean-separate histogram equalization (RMSHE) methods. The proposed method is referred to gain-controllable clipped histogram equalization (GC-CHE) to provide both histogram equalization and brightness preservation. More specifically adaptive contrast enhancement is realized by using clipped histogram equalization with controllable gain. The clipping rate is determined based on the mean brightness, and the clipping threshold is determined based on the clipping rate. The clipping rate is adaptively controlled to enhance the contrast with preserving the mean brightness. It is mathematically proven that the mean brightness of the output image converges to that of the input image with adaptive controlled. Simulation results show that the proposed GC-CHE method outperforms existing histogram-based methods, such as HE, BHE, and RMSHE, in various situations.

License Plate Localization and Character Segmentation With Feedback Self-Learning and Hybrid Binarization Techniques

Abstract: License plate localization (LPL) and character segmentation (CS) play key roles in the license plate (LP) recognition system. In this paper, we dedicate ourselves to these two issues. In LPL, histogram equalization is employed to solve the low-contrast and dynamic-range problems; the texture properties, e.g., aspect ratio, and color similarity are used to locate the LP; and the Hough transform is adopted to correct the rotation problem. In CS, the hybrid binarization technique is proposed to effectively segment the characters in the dirt LP. The feedback self-learning procedure is also employed to adjust the parameters in the system. As documented in the experiments, good localization and segmentation results are achieved with the proposed algorithms.

Color histogram features based image classification in content-based image retrieval systems

Abstract: In content-based image retrieval systems (CBIR) the most efficient and simple searches are the color based searches. Although this methods can be improved if some prepocessing steps are used. In this paper one of the prepocessing algorithms, the image classification is analyzed. In CBIR image classification has to be computationally fast and efficient. In this paper a new approach is introduced, which based on low level image histogram features. The main advantage of this method is the very quick generation and comparison of the applied feature vectors.

Blind forensics of contrast enhancement in digital images

Abstract: Digital images have seen increased use in applications where their authenticity is of prime importance. This proves to be problematic due to the widespread availability of digital image editing software. As a result, there is a need for the development of reliable techniques for verifying an image's authenticity. In this paper, a blind forensic algorithm is proposed for detecting the use of global contrast enhancement operations to modify digital images. Furthermore, a separate algorithm is proposed to identify the use of histogram equalization, a commonly implemented contrast enhancement operation. Both algorithms perform detection by seeking out unique artifacts introduced into an image's histogram as a result of the particular operation examined. Additionally, results are presented showing the effectiveness of both proposed algorithms.

Image equalization based on singular value decomposition

Abstract: In this paper, a novel image equalization technique which is based on singular value decomposition (SVD) is proposed. The singular value matrix represents the intensity information of the given image and any change on the singular values change the intensity of the input image. The proposed technique converts the image into the SVD domain and after normalizing the singular value matrix it reconstructs the image in the spatial domain by using the updated singular value matrix. The technique is called the singular value equalization (SVE) and compared with the standard grayscale histogram equalization (GHE) method. The visual and quantitative results suggest that the proposed SVE method clearly outperforms the GHE method.

A novel approach for contrast enhancement based on Histogram Equalization

Abstract: Histogram based techniques is one of the important digital image processing techniques which can be used for image enhancement. One of the advantages of histogram based techniques is simplicity of implementation of the algorithm. Also it should be mentioned that histogram based techniques is much less expensive comparing to the other methods. Histogram based techniques for image enhancement is mostly based on equalizing the histogram of the image and increasing the dynamic range corresponding to the image. Histogram equalization (HE) method has two main disadvantages which affect efficiency of this method. For solving the above problems, some techniques have proposed for example using bi histogram equalization (BHE) algorithm instead of histogram equalization (HE). It should be mentioned that bi histogram equalization (BHE) is one of the best proposed algorithm which has proposed until now. This paper presents a novel algorithm for contrast enhancement based on histogram equalization (HE). Our proposed algorithm applies some preprocessing steps on the histogram corresponding to the image and then applies histogram equalization. We have applied our proposed algorithm on a database which includes 220 normal images and results are promising. Our proposed method has better results comparing with bi histogram equalization (BHE) algorithm based on visual criterion and a mathematical criterion.

A novel algorithm for initial lesion detection in ultrasound breast images.

Abstract: This paper proposes a novel approach to initial lesion detection in ultrasound breast images. The objective is to automate the manual process of region of interest (ROI) labeling in computer-aided diagnosis (CAD). We propose the use of hybrid filtering, multifractal processing, and thresholding segmentation in initial lesion detection and automated ROI labeling. We used 360 ultrasound breast images to evaluate the performance of the proposed approach. Images were preprocessed using histogram equalization before hybrid filtering and multifractal analysis were conducted. Subsequently, thresholding segmentation was applied on the image. Finally, the initial lesions are detected using a rule-based approach. The accuracy of the automated ROI labeling was measured as an overlap of 0.4 with the lesion outline as compared with lesions labeled by an expert radiologist. We compared the performance of the proposed method with that of three state-of-the-art methods, namely, the radial gradient index filtering technique, the local mean technique, and the fractal dimension technique. We conclude that the proposed method is more accurate and performs more effectively than do the benchmark algorithms considered.

A contrast enhancement method using dynamic range separate histogram equalization

Abstract: In this paper, a contrast enhancement method using dynamic range separate histogram equalization (DRSHE) is proposed. Histogram equalization (HE) method is widely used for contrast enhancement. However, HE is not suitable for consumer electronic products directly because it may cause side-effects such as washed out appearance and false contouring due to the significant change in brightness. DRSHE separates the dynamic range of histogram into k parts and resizes the grayscale range based on its area ratio. Then intensities of histogram are uniformly redistributed in resized grayscale range. DRSHE uses weighted average of absolute color difference (WAAD) in order to emphasize the edge of original image and moderate the histogram variation effectively. We introduced a linear adaptive scale factor to reduce excessive changes in brightness. The experimental results show that DRSHE suppresses unintended changes in brightness and preserves naturalness of the original image compared with conventional methods.

Brightness preserving weight clustering histogram equalization

Abstract: Histogram equalization (GHE) is a simple and widely accepted method for contrast enhancement. Although there are extensions of GHE that can preserve the brightness of the original image better than the original method, these extensions sometimes fail to enhance the visualization of the original image. Therefore, we propose a new method called "Brightness Preserving Weight Clustering Histogram Equalization" (BPWCHE) that can simultaneously preserve the brightness of the original image and enhance visualization of the original image. BPWCHE assigns each non-zero bin of the original image's histogram to a separate cluster, and computes each cluster's weight. Then, to reduce the number of clusters, we use three criteria (cluster weight, weight ratio and widths of two neighboring clusters) to merge pairs of neighboring clusters. The clusters acquire the same partitions as the result image histogram. Finally, transformation functions for each cluster's sub-histogram are calculated based on the traditional GHE method in the new acquired partitions of the result image histogram, and the sub-histogram's gray levels are mapped to the result image by the corresponding transformation functions. We showed experimentally that BPWCHE can preserve image brightness and enhance visualization of images more effectively than GHE and other brightness preserving methods.

Contrast enhancement by automatic and parameter-free piecewise linear transformation for color images

Abstract: Conventional contrast enhancement methods have four shortcomings. First, most of them need transformation functions and parameters which are specified manually. Second, most of them are application-oriented methods. Third, most of them are performed on gray level images. Fourth, the histogram equalization (HE) based enhancement methods use non-linear transform function. Thus, this paper proposes an automatic and parameter-free contrast enhancement algorithm for color images. This method includes following steps: First, RGB color space is transformed to HSV color space. Second, image content analysis is used to analyze the image illumination distribution. Third, the original image is enhanced by piecewise linear based enhancement method. Finally, the enhancement image is transformed back to RGB color space. This novel enhancement is automatic and parameter-free. Our experiments included various color images with low and high contrast. Experiment results show that the performance of the proposed method is better than histogram equalization (HE) and its six variations in non-over enhancement and natural clearly revealed. Moreover, the proposed algorithm can be run on an embedded environment (such as mobile device, digital camera, or other consumer products) and processed in real-time system due to its simplicity and efficiently.

Color image enhancement using brightness preserving dynamic histogram equalization

Abstract: Histogram equalization (HE), although one of the most popular techniques used for digital image enhancement, is not very suitable to be implemented directly in consumer electronics, such as television, because this method tends to produce an output with saturation effect. To overcome this weakness, it is suggested that the mean intensity of the input image be maintained in the output image. Previously, we proposed a method known as brightness preserving dynamic histogram equalization (BPDHE) which can fulfill this requirement for grayscale images. In this paper, we present several possibilities to extend this method for color images.

Optimum Histogram Pair Based Image Lossless Data Embedding

Abstract: This paper presents an optimum histogram pair based image lossless data embedding scheme using integer wavelet transform and adaptive histogram modification. This new scheme is characterized by (1) the selection of best threshold T , which leads to the highest PSNR of the marked image for a given payload, (2) the adaptive histogram modification, which aims at avoiding underflow and/or overflow, is carried out only when it is necessary, and treats the left side and the right side of histogram individually, seeking a minimum amount of histogram modification, and (3) the selection of most suitable embedding region, which attempts to further improve the PSNR of the marked image in particular when the payload is low. Consequently, it can achieve the highest visual quality of marked image for a given payload as compared with the prior arts of image lossless data hiding. The experimental results have been presented to confirm the claimed superior performance.

One step beyond histograms: Image representation using Markov stationary features

Abstract: This paper proposes a general framework called Markov stationary features (MSF) to extend histogram based features. The MSF characterizes the spatial co-occurrence of histogram patterns by Markov chain models, and finally yields a compact feature representation through Markov stationary analysis. Therefore, the MSF goes one step beyond histograms since it now involves spatial structure information of both within histogram bins and between histogram bins. Moreover, it still keeps simplicity, compactness, efficiency, and robustness. We demonstrate how the MSF is used to extend histogram based features like color histogram, edge histogram, local binary pattern histogram and histogram of oriented gradients. We evaluate the MSF extended histogram features on the task of TRECVID video concept detection. Results show that the proposed MSF extensions can achieve significant performance improvement over corresponding histogram features.

Color Traits Transfer to Grayscale Images

Abstract: Here we are presenting some novel techniques for squirting colors in grayscale images. The problem of coloring grayscale images has no exact solution. Here we are attempting to minimize the human efforts needed in manually coloring the grayscale images. We need human interaction only to find a reference color image, then the job of transferring color traits from reference color image to grayscale image is done by proposed techniques. In these techniques, the color palette is prepared using pixel windows of some degrees taken from reference color image. Then the grayscale image is divided into pixel windows with same degrees. For every window of grayscale image the palette is searched for equivalent color values, which could be used to color grayscale window. In the whole process the luminance values of reference color image and target grayscale image are only matched and based on best possible match the respective chromaticity values of color image are transferred to grayscale image. For palette preparation first we used RGB color space and then Kekre's LUV color space[9]. Results with Kekre's LUV color space were comparatively better. To improve the searching time through color palette the exhaustive and Kekre's fast search are used.

Exact Histogram Specification Optimized for Structural Similarity

Abstract: An exact histogram specification (EHS) method modifies its input image to have a specified histogram. Applications of EHS include image (contrast) enhancement (e.g., by histogram equalization) and histogram watermarking. Performing EHS on an image, however, reduces its visual quality. Starting from the output of a generic EHS method, we maximize the structural similarity index (SSIM) between the original image (before EHS) and the result of EHS iteratively. Essential in this process is the computationally simple and accurate formula we derive for SSIM gradient. As it is based on gradient ascent, the proposed EHS always converges. Experimental results confirm that while obtaining the histogram exactly as specified, the proposed method invariably outperforms the existing methods in terms of visual quality of the result. The computational complexity of the proposed method is shown to be of the same order as that of the existing methods. Index terms: histogram modification, histogram equalization, optimization for perceptual visual quality, structural similarity gradient ascent, histogram watermarking, contrast enhancement.

Histogram-Based Color Image Retrieval

Abstract: The theory of image information entropy is adopted as color distribution,and the color spatial distribution entropy is adopted as the spatial descriptor colorThe writer also uses weighted-synthetical method and proportion-coefficient method to indicated the image characteristics and designs a image retrieval algorithm based on color histogram and its color spatial distribution entropyAccording to the analysis and comparison,this kind of algorithm has a very good retrieval ability of image

Automatic Mood-Transferring between Color Images

Abstract: This paper presents a new color-conversion method that offers users an intuitive, one-click interface for style conversion. So, rather than having to supply a reference image, users simply select a color mood with a mouse click. To address the color-transfer quality problem, we associate each color mood with a set of up to 10 images from our image database. After selecting their color mood, users choose one associated image. Our histogram matching algorithm then uses the selected image to determine the input image's color distribution. We thereby achieve more accurate and justifiable color conversion results, while also preserving spatial coherence. Here, we further describe our solutions and their results and compare them to existing approaches.

Display device, display driver and image display method

Abstract: In a display device and a display driver, when a grayscale of a display image is equal to or lower than a specific grayscale value obtained from a histogram of the display image, a display grayscale is extended with a linear function. On the other hand, when a grayscale of a display image is equal to or higher than the specific grayscale value, histogram equalization of a part higher than the specific grayscale value is performed, and the display grayscale is extended with a non-linear function obtained from the histogram equalization.

System for detecting image using t-test and method therefor

Abstract: An image recognition system using t-test and a method thereof are provided to decide efficiently the face image of the respective different image situation on the intelligibility lock through the image situation classification of the face image selecting the recognizer drawing the image situation of the optimum which is suitable for each image situation by classifying the corresponding recognizer using t-test image situations acknowleged through the image recognition of situation module. An image recognition system using t-test comprises the followings: the vision recognition module(110) loading the face image(10); the image normalization module(120) normalizing loaded face image as described above; the image pre-processing module(130) removing the interference included in the normalized face image as described above through the histogram equalization and producing vector data; the image situation classification module(140) divided into the number in which user designates the image situation included in vector data through the clustering using the method for the K-means grouping; the image recognition of situation module(150) calculating average the classified image situation as described above through the Euclidean distance equation based on center and recognizing clearly the image situation based on the calculated mean value; the recognizer fusion module(160) selecting the recognizer which is suitable for each image situation based on the acknowleged image situations as described above through the recognizer classification using t-test.

Content adaptive histogram enhancement

Abstract: This disclosure describes techniques for performing content adaptive histogram enhancement. In accordance with the content adaptive histogram enhancement techniques of this disclosure, a frame of digital image data, e.g., digital video data or digital still image data, is classified into one of a plurality of content classes based on histogram of pixel intensity values of the frame. The content classes may represent various levels of brightness, contrast, or the like. To classify the frame into the corresponding content class, a shape of the histogram may be analyzed using various histogram statistics. Based on the content class of the frame, the pixel intensity values of the frame are mapped to new pixel intensity values.

Method and apparatus for enhancing images

Abstract: A method for enhancing an image, comprising the steps of constructing an input histogram corresponding to the image, the input histogram representing a pixel intensity distribution corresponding to the image; establishing a variable plateau profile to be applied to the input histogram; clipping the input histogram at the variable plateau profile to produce a clipped input histogram; constructing a cumulative histogram from the clipped input histogram; normalizing the cumulative histogram to produce a normalized cumulative histogram; and transforming the image using the normalized cumulative histogram to produce an enhanced output image corresponding to the input image, wherein at least a portion of the input image is enhanced in the output image.

Improving Robustness of Quantization-Based Image Watermarking via Adaptive Receiver

Abstract: In this paper, the watermarking channel is modeled as a generalized channel with fading and nonzero mean additive noise. In order to improve the watermark robustness against the generalized channel, we present an optimized watermark extraction scheme by using an adaptive receiver for quantization-based watermarking. In the proposed extraction scheme, we adaptively estimate the decision zone of the binary data bits and the quantization step size. A training sequence is embedded into the original image together with the informative watermark. The estimation of the decision zone takes advantage of the response function of the training sequence. Compared to those watermarking schemes without receiver adaptation, the main improvement is the enhanced robustness against median filtering, image intensity Direct Current (DC) change, histogram equalization, color reduction, image intensity linear scaling, image intensity nonlinear scaling such as Gamma correction etc.

Image contrast enhancement based on 2D Teager-Kaiser operator

Abstract: This paper describes a new method based on the 2D Teager- Kaiser Energy Operator (2DTKEO) for image contrast enhancement. The 2DTKEO reflects better the local activity than the amplitude of a classical edges detection operator. This quadratic filter is used to enhance high frequency information which is then combined with image gray values to estimate the edge strength value used in the enhancement process. This value is the average of the gray values by the energy activity at each pixel. Different examples of images are provided to demonstrate the Performance of the proposed method is demonstrated on synthetic and real images and the results compared to histogram equalization and to an edge- based contrast method.

A Novel Approach for Contrast Enhancement in Biomedical Images Based on Histogram Equalization

Abstract: This paper presents a novel technique to increase the quality of medical images based on histogram equalization. In the proposed method first we have applied a noise reduction method and then we apply some suitable preprocessing on histogram of the medical images and after that histogram equalization has been applied on the new histogram. Our proposed method in despite of its simplicity has better results in compare to other usual methods based on histogram equalization. The quality of resulted images after applying our proposed methods has been tested on a database (medical images) with a confirmed criterion by viewer. Also we have considered a mathematical criterion for comparing our proposed algorithm with other available methods for contrast enhancement. Results show the better efficiency of the proposed method.

New enhancement algorithm for infrared image based on double plateaus histogram

Abstract: The characteristics of the infrared dim-small targets images were analyzed.A new contrast enhancement algorithm was presented based on double plateaus histogram equalization.By setting a higher threshold value as the upper limit,the algorithm could constrain the background and noises.By setting a lower threshold value as the lower limit,the algorithm could magnify the dim-small targets and the details of the image.Then,the gray intervals of the histogram were equalized.The disadvantages of histogram equalization were overcame with the proposed method.Compared with the algorithm based on single plateau histogram,the new method can enhance image contrast,and preserve image detail simultaneously.