A human visual model based image histogram modification technique

Digital image processing is versatile research in this era. Many researchers implement different types of organizations like image restoration, image enhancement, color image processing, image segmentation etc. Image enhancement technique is among the simplest and most appealing area of digital image processing. Enhancement techniques like brightness preservation, contrast enhancement highlight certain features means depend which part of the image want to be enhance some application some input image including noise, reduction or removal of noise is also form of image enhancement. Brightness preservation has enhanced visual quality of digital image so that the limitation contained in these images is used for various applications in a better way. A very popular technique for image enhancement is histogram equalization (HE) and curvelet transformation. HE technique is commonly employed for image enhancement because of its simplicity and comparatively better performance on almost all types of images. Another widely used technique is curvelet transformation. This technique is identified and separate bright regions of image but more error rate and low peak signal to noise ratio(PSNR), result of this technique is brightness preservation level is low and output image is gray. This paper design a hybrid model through discrete cosine transformation, discrete wavelet transformation and combine output of both techniques with image fusion. Proposed algorithm enhanced features and removal noise by decomposition of image using DWT and discrete cosine transformation, adaptive histogram equalization is very important part in this algorithm for smooth image. The tested results of different images are comparing with previous method, generating result with different parameters; less mean square error and high PSNR for improve the quality of an image. This paper presents a hybrid model used various parameter for enhance images like satellite images, medical images etc.

37 - The Applications of Fuzzy Techniques to Image Processing Based on the Human Visual System Model

This study aims to detect the presence of chicken egg embryos using digital image processing. The detection of chicken egg embryos was processed by image segmentation using the thresholding method. The implementation of this method needs to be supplemented with preprocessing phase using methods such as cropping, grayscaling, intensity adjustment, and histogram equalization (HE). The critical emphasis in this research is on image enhancement using a hybrid of Contrast Limited Additive Histogram Equalization (CLAHE) and HE. Based on comparing CLAHE, HE, hybrid CLAHE-HE, or HE-CLAHE, the optimal result used as input for the segmentation process is hybrid CLAHE-HE. The segmentation results using the thresholding method and hybrid CLAHE-HE showed clear and characteristic pictures of chicken egg embryos incomplete detection. Other methods, such as Otsu and k-means, also showed clear and characteristic pictures of chicken egg embryos. However, in terms of processing speed, the thresholding method was faster than the Otsu and k-means methods. The speed comparisons are thresholding<Otsu<k-means (0.57; 0.72; and between 10-10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sup> seconds). These experiments show that our proposed method (thresholding and hybrid CLAHE-HE) can divide/segment embryonic eggs with significant embryonic characteristics and patterns.

Image enhancement, aimed at improving the image contrast and information quality, is one of the most critical steps in image processing. Due to insufficient enhancement and the mean shift problem of conventional image enhancement techniques, new artificial intelligence-based image enhancement approaches have become an inevitable need in image processing. This paper employs the krill herd algorithm (KHA) and particle swarm optimization (PSO) to suggest a novel hybrid approach, called (PSOKHA) for image enhancement. The suggested PSOKHA method is used in search of optimum transfer function parameters to increase the quality of the images. For comparative evaluation, the performance of the PSOKHA is compared with six latest successful enhancement methods: PSO, KHA, screened Poisson equation (SPE), histogram equalization (HE), brightness preserving dynamic fuzzy HE (BPDFHE), and adaptive gamma correction weighted distribution (AGCWD). Experiments results in testing images include a medical image, a satellite image, and a handwritten image, demonstrate that the suggested strategy can produce better enhanced images in terms of several measurement criteria such as contrast, PSNR, entropy, and structure similarity index (SSIM).

Logarithmic transformation and histogram equalization (HE) are well-known image enhancement techniques in spatial domain. These techniques are very popular for contrast enhancement because the methods are simple and effective. The basic idea of HE is to remap the gray levels of an image. However, HE tends to introduce some annoying artifacts and unnatural enhancement. Here, we propose an integration of the two techniques at pixel level, doing first the HE and then using logarithmic transformation for mapping purpose. Both the global HE and local HE are preformed on the input image pixels. The histogram equalization has been performed in the MATLAB environment. We have experimented the proposed techniques over a number of sample images and found to produce much better results compared to image enhancement using the inbuilt MATLAB function histeq.

This article aims to test FOS (first-order statistical) in extracting features of embryonated eggs. This test uses the initial step of image processing to get the best input image in feature extraction. The image processing method starts from the image acquisition process, then improves with image preprocessing and segmentation. Image acquisition in this study uses the concept of egg candling in a dark place captured with a smartphone camera. The acquisition results are improved by image preprocessing using gray scaling, image enhancement (by Histogram Equalization), and segmentation of chicken egg image. The segmentation results were extracted using FOS with five parameters: mean, entropy, variance, skewness, and kurtosis. Based on the calculation of these parameters, it is graphed and shows the difference in patterns between fertile and infertile eggs. However, some eggs have a similar pattern, thus affecting the identification process. The identification process used neural networks by the backpropagation method for training and testing. The training results provide an accuracy value of 100% of all training data; however, 80% of the new test data obtained test results at testing. This test is carried out with 100 data, 50 each for training and test data. Based on the test results, which significantly affect the level of accuracy is the feature extraction method. FOS pattern in detecting the fertility of chicken eggs by BP Neural Network is still categorized as low, so it is necessary to improve methods to get maximum results.

A new approach of sleep and feeding behaviors in the laboratory rat

Introduction and objective: Craniosynostosis is a pathological condition resulting from premature closure of cranial sutures. Open cranial vault reconstruction has been the standard of care for isolated non-syndromic craniosynostosis. The current centre introduced the endoscopic craniosynostosis surgery in 2015. The aim of the study is to compare safety outcomes of endoscopic versus open surgery in craniosynostosis within the context of a developing healthcare system, with a focus on anterior synostosis. Methods: This was a retrospective study of all patients undergoing surgery for anterior craniosynostosis from 2012 to 2018 at a single institution. Data on age at surgery, demographics, sutures, surgery duration and complications, including follow up information was obtained. We excluded syndromic patients and patients with pan-synostosis. Statistical analysis was undertaken with P value set at &lt;.05. Results: There were totally 40 patients who underwent craniosynostosis surgery. Seventeen patients underwent endoscopic correction, and 23 open procedures. Metopic and coronal suture patients were equally distributed, with an equal sex distribution. The mean age at surgery in the endoscopic group was 3.7 months (3-5) and higher at 11.5 months (8-18) in the open group ( P &lt; .05). The duration of surgery, blood loss and overall length of hospital stay were significantly reduced in the endoscopic group ( P &lt; .05). In the endoscopic cohort, patients with metopic synostosis obtained complete correction, while patients with unicoronal synostosis had minor residual frontal asymmetry. There were no immediate post-operative complications in the endoscopic group, but the open surgery cohort had minor dural tears in 6 patients. Conclusion: The endoscopic technique has a better safety profile than open surgery for craniosynostosis and results in a complete correction in patients with metopic and bicoronal craniosynostosis.

Language function distribution in left-handers: A navigated transcranial magnetic stimulation study

Principle objective of Image enhancement is to process an image so that result is more suitable than original image for specific application. image enhancement used in Quality Control ,Problem Diagnostics, Research and Development ,Insurance Risk Assessment ,Risk Management Programme, Digital infrared thermal imaging in health care, Surveillance in security, law enforcement and defence. Various enhancement schemes are used for enhancing an image which includes gray scale manipulation, filtering and Histogram Equalization (HE), fast Fourier transform. Image enhancement is the process of making images more useful. The reasons for doing this include, Highlighting interesting detail in images, removing noise from images, making images more visually appealing, edge enhancement and increase the contrast of the image.

This paper presents a new method of histogram equalization for grayscale images, which is called the gradient based histogram equalization. The histogram equalization is performed on the image filtered by means of gradient operators. The proposed method is simple, fast, and the preliminary experimental examples with different images show that the method is effective for image enhancement. While preserving the range and mean intensity of the image, the new method allows for reducing the standard deviation and significantly straitening the graph of the histogram, when comparing with the traditional (or global) histogram equalization.

Today, there is almost no area of technical endeavor that is not impacted in some way or the other by digital image processing. The principle objective of enhancement is to process an image so that the result is more suitable than original image for specific application. When an image is processed for visual interpretation, the viewer is the ultimate judge of how well a particular method works. Histograms, basis for numerical spatial domain processing techniques provides useful image statistics. These are simple to calculate in software and also lend themselves to economic hardware implementations, thus making them a popular tool for real-time image processing. The transformation or mapping of each pixel of input image into a corresponding pixel of the processed output image is called as “Histogram Equalization”. It is “automatic”, the process which is based on information that can be extracted directly from given image, without the need for further parameter specifications. Diabetes mellitus is a metabolic disorder that characterized by inability of the pancreas to control blood glucose concentration. 80% of deaths in Diabetic patients are due to heart diseases. The main objective of this paper is to observe enhancement of Electron micrograph image of a myocardial capillary from a diabetic patient, and to get image information, pixel regions, Contrast Limited Adaptive Histogram Equalization (CLAHE) and intensity adjustment which mainly helps to educate the diabetic patients to prevent cardiac complications i.e., Diabetic Cardiomyopathy using MATLAB.

Image enhancement is the task of applying certain alterations to an input image such as to obtain a more visually pleasing image. The alteration usually requires interpretation and feedback from a human evaluator of the output resulting image. Therefore, image enhancement is considered a difficult task when attempting to automate the analysis process and eliminate the human intervention. Furthermore, images that do not have uniform brightness pose a challenging problem for image enhancement systems. Different kinds of histogram equalization techniques have been employed for enhancing images that have overall improper illumination or are over/under exposed. However, these techniques perform poorly for images that contain various regions of improper illumination or improper exposure. In this paper, we introduce new human vision model based automatic image enhancement techniques, multi-histogram equalization as well as local and adaptive algorithms. These enhancement algorithms address the previously mentioned shortcomings. We present a comparison of our results against many current local and adaptive histogram equalization methods. Computer simulations are presented showing that the proposed algorithms outperform the other algorithms in two important areas. First, they have better performance, both in terms of subjective and objective evaluations, then that currently used algorithms on a series of poorly illuminated images as well as images with uniform and non-uniform illumination, and images with improper exposure. Second, they better adapt to local features in an image, in comparison to histogram equalization methods which treat the images globally.

Non-linear filters have proved to be powerful tools in image analysis, enhancement and restoration. Filters such as the ranked order, median, alpha-trimmed mean, weighted median and the class of morphological filters have been investigated thoroughly and have been shown to be indispensable in certain image processing applications. Another interesting, though unrelated, research area has been the use of fuzzy set theoretic operations in image processing. The underlying idea in employing the fuzzy set theory is to employ fuzzy membership grades to represent image characteristics and features. This paper seeks to unify these two methodologies resulting in an interesting approach to image enhancement problems. The emphasis is on using non-linear operations to generate a local fuzzy property plane. This is then used as an adaptive filter based on local image characteristics. In this paper, we propose a novel localized filtering approach similar to non- linear filters, the point of departure being that we work in the fuzzy property plane rather than the image domain itself. For instance, we may realize a fuzzy implementation based on the median filter which traditionally uses actual pixel intensities, to generate fuzzy memberships instead. In our experiments, we consider a locally adaptive contrast enhancement of x-ray images typically having low contrast. The results are compared with traditional enhancement techniques, such as histogram equalization. The results provide a better subjective quality as compared to other approaches as is also evident from the histogram distribution of the processed images.© (1996) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Underwater images suffer from color cast and low visibility caused by the medium scattering and absorption, which will reduce the use of valuable information from the image. In this paper, we propose a novel method which includes four stages of pixel intensity center regionalization, global equalization of histogram, local equalization of histogram and multi-scale fusion. Additionally, this method uses a pixel intensity center regionalization strategy to perform centralization of the image histogram on the overall image. Global equalization of histogram is employed to correct color of the image according to the characteristics of each channel. Local equalization of dual-interval histogram based on average of peak and mean values is used to improve contrast of the image according to the characteristics of each channel. Dual-image multi-scale fusion to integrate the contrast, saliency and exposure weight maps of the color corrected and contrast enhanced images. Experiments on variety types of degraded underwater images show that the proposed method produces better output results in both qualitative and quantitative analysis, thus, the proposed method outperforms other state-of-the-art methods.