Histogram Modification Technique Research Articles

Experimental and Numerical Research of Paved Microcrack Using Histogram Equalization for Detection and Segmentation

The paved cracks are one of the major concerns for scientists and engineers in road maintenance and damage evaluation study. Digital image processing applications have been applied in road surface inspection, classification, and decomposition of paved roads. This paper has tested and proposed the process to evaluate the road cracks and their possible solution as we know that the key issues for analysis are enhancement and segmentation of image along with edge detection to attain the promising results we have gained and discussed under the heading of simulations for the experimental and numerical of crack detection. Using MATLAB, we examine the various gray-level image using better techniques based on their computational capability. The method is based upon one of the histogram modification techniques, which is coupled with the segmentation method and the crack edge detection. At last, three feature methods are used, namely, Harris, MSERF, and SURF, to wind up our research.

A multi-layered “plus-minus one” reversible data embedding scheme

Reversible Data Hiding (RDH) is one of the most well-known methods adopted for concealed communication. One of the major issues in RDH is the overflow/underflow which exists in almost all the embedding techniques. This is a major issue which hinders the hiding capacity in many existing techniques. In the proposed novel LSB ODD/EVEN embedding algorithm, neither histogram modification (which involves shifting) nor difference expansion (which involves shifting and expansion of error) techniques are involved. No overflow/underflow problem is reported in the proposed method and involves multilayer embedding. Performance of this approach or any data hiding schemes are usually measured in terms of MSE, PSNR, and SSIM. After multiple layer of embedding the PSNR for 1.5 bpp stands at 43 dB approximately. This approach is tested over standard test images that includes medical images also and compared with well-known existing schemes.

Fast Fusion-Based Dehazing With Histogram Modification and Improved Atmospheric Illumination Prior

Haze can seriously affect the visible and visual quality of outdoor optical sensor systems, e.g., driving assistance, remote sensing, and video surveillance. Single image dehazing is an intractable problem due to its ill-posed nature. The main idea of the paper is combining multi-scale fusion strategy and prior knowledge, thereby presenting balanced image contrast enhancement and intrinsic color preservation, efficiently. The atmospheric illumination prior (AIP) has been proved that haze mainly degrades the contrast of the luminance channel rather than chrominance channels in YCrCb colorspace. To this end, we firstly identify and remove the color veil (unbalanced color channel) with the white balance algorithm, to reduce the influence of unbalanced color channels neglected by the AIP. Considering the new observation that hazy regions exhibit low contrast with high-intensity pixels, the dense and mild haze are enhanced by a set of histogram modification techniques, respectively. Then, with the derived inputs, multi-scale fusion based on Laplacian decomposition strategy is proposed to blend visual contrast only in the luminance channel. Without relying on complex enhancement algorithms and only dealing with one channel, the proposed method is attractive for real-time applications. Moreover, The proposed method can be directly applied to the video sequences frame by frame, alleviating visual artifacts. The simulation results show that our method is comparative to and even better than the more complex state-of-the-art techniques.

Efficient reversible data hiding based on the histogram modification of differences of pixel differences

Histogram modification is one of the most successful reversible data hiding methods. Among many histogram-modification techniques, difference-histogram-based methods perform well, but the efficiency of difference-histogram-based methods is still unsatisfactory. In this paper, we propose a novel reversible data hiding method based on the differences of pixel differences (DPD). Our method consists of two parts. In the first part, the DPD of cover image is computed to generate the DPD histogram. DPDs that are greater than the position of the peak point of DPD histogram are shifted to the right. Then the secret data is embedded into the pixels associated with the peak point. In the second part, we compute the DPD of the pixel sequence generated by the first part to generate a DPD histogram and shift DPDs that are less than the position of the peak point to the left. As the secret data is embedded into the pixels related to the peak point, the higher the peak point is, the more messages can be embedded. Compared with difference histogram, DPD histogram has a higher peak point, so the embedding capacity is increased. What’s more, we extend the proposed method to a high-embedding-capacity method. Experimental results demonstrate the superior performance of our method and the extended form.

High Capacity Reversible Data Hiding Based on Multiple Histograms Modification

Among various techniques of reversible data hiding (RDH), histogram modification has been mostly investigated and used in practice. Recently, a new histogram modification technique, namely, multiple histograms modification (MHM) has been proposed and received the increasing attentions. By MHM, multiple histograms can be modified differently to achieve an excellent embedding performance. However, as the maximum modification on each pixel value is one, only one pair of bins in the prediction-error histogram (PEH) is allowed to be expanded for data embedding. This drawback leads to a low capacity and limits its extension for high capacity scenario. To remedy this, we extend the MHM scheme and propose an efficient solution for the high-capacity embedding. Specifically, instead of only selecting one pair of expansion bins in each PEH, in the proposed method, multiple pairs of bins are utilized for expansion in each PEH. Moreover, the embedding performance is optimized by an advisable expansion bin selection strategy. Consequently, the multiple PEHs are allowed to embed with different capacities according to the sharpness of distribution, i.e., the low-index PEH that consists of more smooth pixels is embedded with a larger capacity than the high-index one. Besides, a simplified parameter determination is developed to seek the optimal solution with a good computational efficiency. The experimental results show that the proposed method can achieve the high capacity and provide a better performance than some state-of-the-art RDH methods.

A logarithmic law based histogram modification scheme for naturalness image contrast enhancement

In this paper, a new logarithmic histogram modification technique for image contrast enhancement with naturalness preservation has been proposed. Traditional histogram equalization scheme usually causes extreme contrast enhancement, which results in unnatural look and artifacts. The proposed technique first enhances the contrast of the image globally through addition and logarithmic law based modification scheme, thereafter the local details of the image are emphasized through the coefficient scaling directly in the compressed domain using discrete cosine transformation. The proposed method can enhance the image contrast uniformly with less number of parameters without losing its basic features. Experimental results show that the proposed method preserves the natural appearance of the image and yields better perceptual quality as compared to the state-of-the-art techniques.

Data Hiding in Color Images Using Huffman Coding and Histogram Modification

Data hiding in digital images provides a secure communication medium by embedding the payload in a cover image without compromising the image quality. In this work, a secure and high-capacity data hiding scheme is proposed using efficient compression, encryption, and steganography techniques. Dictionary-based coding along with Huffman coding is used to compress the secret data and hence increase the embedding capacity. Two encrypted keys are generated to control the data decompression and hence improve the level of security. In addition, a block-based histogram modification technique is applied to embed the compressed data into the chrominance components of the cover image to minimize the image distortion. The results indicate that the proposed scheme achieves high embedding capacity without degrading the quality of the cover image.

A robust reversible image watermarking scheme in DCT domain using Arnold scrambling and histogram modification

Among the various watermarking schemes, the reversible watermarking scheme has drawn extensive attention in the recent years for its application in sensitive issues like medical, military and typical law-enforcement images. Cover image dependent embedding capacity and lack of robustness are the most crucial concerns of the reversible watermarking methods. To overcome these issues, a discrete cosine transform (DCT) and histogram shifting based robust reversible image watermarking scheme using Arnold scrambling is presented in this paper. Initially, the image is decomposed into non-overlapping blocks and consequently DCT are applied to each block to embed a binary bit of watermark into each transformed block by modifying one pair of middle significant AC coefficients. In this initial step, location map is also generated for the cover image restoration purpose in the extracting side. Then, this location map is embedded in the cover image using histogram modification technique. In the receiver side, at first location map is generated from an image using histogram modification method and watermark is recovered from the corresponding image. Using location map reversible image is reversed in the following extracting phase of the proposed method. The proposed reversible watermarking scheme has also been experimented to verify the robustness property against several image processing attacks and satisfactory results are achieved.

A robust reversible image watermarking scheme in DCT domain using Arnold scrambling and histogram modification

Among the various watermarking schemes, the reversible watermarking scheme has drawn extensive attention in the recent years for its application in sensitive issues like medical, military and typical law-enforcement images. Cover image dependent embedding capacity and lack of robustness are the most crucial concerns of the reversible watermarking methods. To overcome these issues, a discrete cosine transform (DCT) and histogram shifting based robust reversible image watermarking scheme using Arnold scrambling is presented in this paper. Initially, the image is decomposed into non-overlapping blocks and consequently DCT are applied to each block to embed a binary bit of watermark into each transformed block by modifying one pair of middle significant AC coefficients. In this initial step, location map is also generated for the cover image restoration purpose in the extracting side. Then, this location map is embedded in the cover image using histogram modification technique. In the receiver side, at first location map is generated from an image using histogram modification method and watermark is recovered from the corresponding image. Using location map reversible image is reversed in the following extracting phase of the proposed method. The proposed reversible watermarking scheme has also been experimented to verify the robustness property against several image processing attacks and satisfactory results are achieved.

A New Breast Border Extraction and Contrast Enhancement Technique with Digital Mammogram Images for Improved Detection of Breast Cancer

Purpose:Breast cancer can be cured if diagnosed early, with digital mammography which is one of the most effective imaging modalities for early detection. However mammogram images often come with low contrast, high background noises and artifacts, making diagnosis difficult. The purpose of this research is to preprocess mammogram images to improve results with a computer aided diagnosis system. The focus is on three preprocessing methods: a breast border segmentation method; a contrast enhancement method; and a pectoral muscle removal method.Methods:The proposed breast border extraction method employs a threshold based segmentation technique along with a combination of morphological operations. The contrast enhancement method presented here is divided into two phages. In phase I, a bi-level histogram modification technique is applied to enhance the image globally and in phase II a non-linear filter based on local mean and local standard deviation for each pixel is applied to the histogram modified image. The pectoral muscle removal method discussed here is implemented by applying a region growing algorithm.Results:The proposed techniques are tested with the Mini MIAS dataset. The breast border extraction method is applied to 322 images and achieved 98.7% segmentation accuracy. The contrast enhancement method is evaluated based on quantitative measures like measure of enhancement, absolute mean brightness error, combined enhancement measure and discrete entropy. The proposed contrast enhancement method when applied to 14 images with different types of masses, the quantitative measures showed an optimum level of contrast enhancement compared to other enhancement methods with preservation of local detail. Removal of the pectoral muscle from MLO mammogram images reduced the search region while identifying abnormalities like masses and calcification.Conclusions:The preprocessing steps proposed here show promising results in terms of both qualitative and quantitative analysis.

Enhancement of Under-Exposed Image for Object Tracking Algorithm Through Homomorphic Filtering and Mean Histogram Matching

Object tracking through video or image becomes more popular in recent years. Indeed, clear and high contrast images are essential to attain good tracking results. The problem arises when the object in an image or video is under-exposed, resulting it to be hardly visible and differentiated from the background. Existing methods are able to solve some of the aforementioned problems, but produce other problems such as over-enhanced effect and color distortion. Thus, the object of interest may become untraceable due to these distortions. This paper proposes an image enhancement method to improve under-exposed images or videos through homomorphic filtering and mean histogram matching, in order to produce more visible and traceable objects. This method integrates homomorphic filtering method and histogram modification technique which consists of histogram matching and dual-histogram stretching. The proposed method is designed to reduce non-uniform illumination while increasing image/video contrast and visibility. The experiment results show that the proposed method outperforms some state-of-the-art methods in terms of visibility and contrast level on some standard benchmark database.

Reversible data hiding in encrypted AMBTC images

Signal processing in the encrypted domain has attracted a lot of attention due to the requirement for content security and privacy protection. Reversible data hiding in encrypted images (RDH-EI) is also a hot topic. However, the majority of the published techniques are designed for uncompressed images rather than JPEG-, VQ- and BTC-compressed images. In this paper, for the first time, a RDH-EI method for AMBTC images is proposed. In the proposed method, the higher mean and lower mean of a triple in an AMBTC-compressed image are encrypted by using stream cipher at first. Then, additional data can be embedded into the redundant space by using prediction error histogram modification technique. Experimental results and analysis demonstrate that, with the marked cipher-image, legal receivers are able to extract embedded data exactly by using a data hiding key, decrypt it to recover an image very similar to the original one by using an image encryption key, or extract additional data and recover the original image error free with both keys. The proposed method is applicable to real-time transmission due to the simple implementation of the algorithm and low computational complexity.

A Method for Microcalcifications Detection in Breast Mammograms.

Breast cancer is the most cause of death for women above age 40 around the world. In this paper, we propose a method to detect microcalcifications in digital mammography images using two-dimensional Discrete Wavelets Transform and image enhancement techniques for removing noise as well as to get a better contrast. The initial step is applying a preprocessing techniques to improve the edge of the breast and then segmentation process (Region of interest) for eliminating some regions in the image, which are not useful for the mammography interpretation. Then unsharp masking and histogram modification technique has used to enhance the contrast of the image and to clarify some details like microcalcifications. Lastly, Discrete Wavelets Transform applied for detecting the abnormality. The proposed method has evaluated using the Mammographic Image Analysis Society (AS) mammography databases. The proposed method has achieved acceptable results.

A novel pre-processing technique for improving image quality in digital breast tomosynthesis.

Nonlinear pre-reconstruction processing of the projection data in computed tomography (CT) where accurate recovery of the CT numbers is important for diagnosis is usually discouraged, for such a processing would violate the physics of image formation in CT. However, one can devise a pre-processing step to enhance detectability of lesions in digital breast tomosynthesis (DBT) where accurate recovery of the CT numbers is fundamentally impossible due to the incompleteness of the scanned data. Since the detection of lesions such as micro-calcifications and mass in breasts is the purpose of using DBT, it is justified that a technique producing higher detectability of lesions is a virtue. A histogram modification technique was developed in the projection data domain. Histogram of raw projection data was first divided into two parts: One for the breast projection data and the other for background. Background pixel values were set to a single value that represents the boundary between breast and background. After that, both histogram parts were shifted by an appropriate amount of offset and the histogram-modified projection data were log-transformed. Filtered-backprojection (FBP) algorithm was used for image reconstruction of DBT. To evaluate performance of the proposed method, we computed the detectability index for the reconstructed images from clinically acquired data. Typical breast border enhancement artifacts were greatly suppressed and the detectability of calcifications and masses was increased by use of the proposed method. Compared to a global threshold-based post-reconstruction processing technique, the proposed method produced images of higher contrast without invoking additional image artifacts. In this work, we report a novel pre-processing technique that improves detectability of lesions in DBT and has potential advantages over the global threshold-based post-reconstruction processing technique. The proposed method not only increased the lesion detectability but also reduced typical image artifacts pronounced in conventional FBP-based DBT.

Lossless data hiding for absolute moment block truncation coding using histogram modification

This paper presents a lossless data hiding method for an absolute moment block truncation coding (AMBTC) images, which is a compressed grayscale image. It is not easy to hide secret data in an AMBTC-compressed image because it is composed of bit planes. Thus, it is very sensitive to change some pixels. Nevertheless, to improve the hiding capacity, we present an efficient extension of the histogram modification technique by counting the coefficients of the bit planes in each 4 × 4 block. In addition, our proposed scheme has low complexity and achieves a high embedding capacity with the good perceptual quality compared to the prior arts. In an experiment, we verified our proposed data hiding scheme through the comparison with previous methods.

High-fidelity reversible data hiding scheme based on multi-predictor sorting and selecting mechanism

Reversible data hiding can completely recover the cover image without any distortion after the secret data is retrieved. In this paper, a new high-fidelity reversible data hiding scheme is proposed using the prediction-error histogram modification technique. A multi-predictor sorting and selecting mechanism is proposed to select an optimum predictor from a set of predictors. Then a smaller prediction-error is obtained to embed the secret data using the prediction-error histogram modification technique. Since the multi-predictor sorting and selecting mechanism can utilize the advantage of multi-predictor and information of current predicted pixel to obtain a more accurate prediction value, our proposed scheme can introduce less image distortion at the same embedding payload. Experimental results demonstrate that our proposed scheme outperforms the similar reversible data hiding schemes.

A novel embedding technique for lossless data hiding in medical images employing histogram shifting method

In this paper, a lossless data hiding method based on histogram shifting for MR images using Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) are presented. In this method, the algorithms are validated to hide the data in wavelet coefficients of high frequency subbands. This scheme has the advantage of comparing the DCT coefficients and the DWT coefficients which permit low distortion between the watermarked image and the original image. It also shifts a part of the histogram of high frequency subbands and embeds the data by using the created histogram zero point. To prevent the overflows and underflows in the spatial domain, caused by the modification of the DCT coefficients and the DWT coefficients, the histogram modification technique is applied. Therefore, we present a validated method to evaluate and compare the performance of DWT and DCT on task, in terms of data embedding payload and the Peak Signal to Noise Ratio (PSNR) in the medical image. A careful experimental analysis validates the method showing its superiority over the existing methods.

히스토그램 변형을 이용한 HDR 영상 렌더링을 위한 인지기반 톤 맵핑 기법

In this paper, we present a perception-based tone mapping technique using histogram modification for displaying high dynamic range image. HDR (high dynamic range) tone mapping algorithms are used to display HDR image on LDR (low dynamic rnage) devices. Although perception-based tone mapping methods provides better performance, it dose not always produce good results for a wide variety of images. The proposed method reduces dynamic range by using the perception-based tone mapping function and histogram modification. A derivative of perception-based tone mapping function is used as constraint function of histogram and additional compensation process is performed. This method not only improves contrast by adopting different constraints on each pixel value, but also preserves more visual details. In order to prevent over enhancement, histogram modification technique is applied. Furthermore, it can control the rate of image contrast using control parameters. Subjective and objective evaluations show that proposed algorithm is better than existing algorithms.

Image interpolating based data hiding in conjunction with pixel-shifting of histogram

Information hiding is an important research issue in digital life. In this paper, we propose a two-stage data hiding method with high capacity and good visual quality based on image interpolation and histogram modification techniques. At the first stage, we first generate a high-quality cover image using the developed enhanced neighbor mean interpolation and then take the difference values from input and cover pixels as a carrier to embed secret data. In this stage, our proposed scheme raises the image quality a lot due to the ENMI method. At the second stage, a histogram modification method is applied on the difference image to further increase the embedding capacity and preserve the image quality without distortion. Experimental results indicate that the proposed method have better PSNR value of stego-image with improving 43 % on the average when compared the past key-studies.

Reversible data hiding scheme based on neighboring pixel differences

In this paper, we propose a reversible data hiding algorithm for grayscale images. Specifically, our algorithm is based on the histogram modification technique. The premise of this algorithm is that a histogram is constructed from the differences between each pixel and its neighbors. In the data embedding process, a modified histogram shifting scheme is used to embed a secret message into the pixels whose pixel difference is located at the peak value within the histogram. Experimental results show that our algorithm can achieve higher embedding capacity and imperceptible distortion. Performance comparisons with other existing algorithms are also provided to demonstrate the feasibility of our proposed algorithm in reversible data hiding.