Iterative Thresholded Bi-Histogram Equalization for Medical Image Enhancement

Enhancement of human vision to get an insight to information content is of vital importance. The traditional histogram equalization methods have been suffering from amplified contrast with the addition of artifacts and a surprising unnatural visibility of the processed images. In order to overcome these drawbacks, this paper proposes interative, mean, and multi-threshold selection criterion with plateau limits, which consist of histogram segmentation, clipping and transformation modules. The histogram partition consists of multiple thresholding processes that divide the histogram into two parts, whereas the clipping process nicely enhances the contrast by having a check on the rate of enhancement that could be tuned. Histogram equalization to each segmented sub-histogram provides the output image with preserved brightness and enhanced contrast. Results of the present study showed that the proposed method efficiently handles the noise amplification. Further, it also preserves the brightness by retaining natural look of targeted image.Comment: 8 Pages, 8 Figures, International Journal of Computer Applications (IJCA

Enabling Pedestrian Safety using Computer Vision Techniques: A Case Study of the 2018 Uber Inc. Self-driving Car Crash

Human lives are important. The decision to allow self-driving vehicles operate on our roads carries great weight. This has been a hot topic of debate between policy-makers, technologists and public safety institutions. The recent Uber Inc. self-driving car crash, resulting in the death of a pedestrian, has strengthened the argument that autonomous vehicle technology is still not ready for deployment on public roads. In this work, we analyze the Uber car crash and shed light on the question, "Could the Uber Car Crash have been avoided?". We apply state-of-the-art Computer Vision models to this highly practical scenario. More generally, our experimental results are an evaluation of various image enhancement and object recognition techniques for enabling pedestrian safety in low-lighting conditions using the Uber crash as a case study.Comment: 10 pages, 8 figures, 3 table

Performance Analysis of HE Methods for Low Contrast Images

AbstractThe image enhancement is one of the important issues in image processing. The main purpose is to highlight certain characteristic of image such as: contrast, sharpening. Histogram equalization is the well-known method for image enhancement. Histogram equalization became a popular technique because it is simple and effective. However Histogram equalization cause excessive contrast enhancement which cause visual artifacts of processed image. In this paper new forms of histogram equalization are overviewed to overcome this drawback. The major difference among the methods is the way to divide the input histogram. Recursive exposure based sub-image histogram equalization (R_ESIHE) use average intensity value as the separating point. Median-mean based sub-image clipped histogram equalization (MMSICHE) and Quadrants dynamic histogram equalization for contrast enhancement (QDHE) use median intensity value as separating point. Here objective parameters are Peak signal to noise ratio (PSNR) and Absolute Mean Brightness Error (AMBE)used to compare the quality of enhancement

An Adaptive Fuzzy Contrast Enhancement Algorithm with Details Preserving

This paper modifies the Adaptive Contrast Enhancement Algorithm with Details Preserving (ACEDP) technique by integrating a fuzzy element in the image type selection. The proposed technique, named the Adaptive Fuzzy Contrast Enhancement with Details Preserving (AFCEDP) technique, first computes the degree of membership of the input image to three categories, i.e. low-, middle- or high-level images. The AFCEDP technique then clips the histogram at different plateau limits that are computed from both the degree of membership and the clipping functions. The classification of an image in the ACEDP technique is done based solely on the intensity range of the maximum number of pixels, which may be inaccurate. In the proposed AFCEDP technique, the image type classification is handled in a better way with the integration of a fuzzy element. The performance of the proposed AFCEDP technique was compared with the conventional ACEDP technique and several state-of-art techniques described in the literature. The simulation results revealed that the AFCEDP technique demonstrates good capability in contrast enhancement and detail preservation. In addition, the experiments using cervical cell images and HEp-2 cell images showed great potential of the AFCEDP technique as a technique for enhancing medical microscopic images

Gaussian mixture model-based contrast enhancement

In this study, a method for enhancing low-contrast images is proposed. This method, called Gaussian mixture model-based contrast enhancement (GMMCE), brings into play the Gaussian mixture modelling of histograms to model the content of the images. On the basis of the fact that each homogeneous area in natural images has a Gaussian-shaped histogram, it decomposes the narrow histogram of low-contrast images into a set of scaled and shifted Gaussians. The individual histograms are then stretched by increasing their variance parameters, and are diffused on the entire histogram by scattering their mean parameters, to build a broad version of the histogram. The number of Gaussians as well as their parameters are optimised to set up a Gaussian mixture modelling with lowest approximation error and highest similarity to the original histogram. Compared with the existing histogram-based methods, the experimental results show that the quality of GMMCE enhanced pictures are mostly consistent and outperform other benchmark methods. Additionally, the computational complexity analysis shows that GMMCE is a low-complexity method

Contrast Enhancement of Brightness-Distorted Images by Improved Adaptive Gamma Correction

As an efficient image contrast enhancement (CE) tool, adaptive gamma correction (AGC) was previously proposed by relating gamma parameter with cumulative distribution function (CDF) of the pixel gray levels within an image. ACG deals well with most dimmed images, but fails for globally bright images and the dimmed images with local bright regions. Such two categories of brightness-distorted images are universal in real scenarios, such as improper exposure and white object regions. In order to attenuate such deficiencies, here we propose an improved AGC algorithm. The novel strategy of negative images is used to realize CE of the bright images, and the gamma correction modulated by truncated CDF is employed to enhance the dimmed ones. As such, local over-enhancement and structure distortion can be alleviated. Both qualitative and quantitative experimental results show that our proposed method yields consistently good CE results

An Adaptive Fuzzy Contrast Enhancement Algorithm with Details Preserving

This paper modifies the Adaptive Contrast Enhancement Algorithmwith Details Preserving (ACEDP) technique by integrating a fuzzy element inthe image type selection. The proposed technique, named the Adaptive FuzzyContrast Enhancement with Details Preserving (AFCEDP) technique, firstcomputes the degree of membership of the input image to three categories, i.e.low-, middle- or high-level images. The AFCEDP technique then clips thehistogram at different plateau limits that are computed from both the degree ofmembership and the clipping functions. The classification of an image in theACEDP technique is done based solely on the intensity range of the maximumnumber of pixels, which may be inaccurate. In the proposed AFCEDP technique,the image type classification is handled in a better way with the integration of afuzzy element. The performance of the proposed AFCEDP technique wascompared with the conventional ACEDP technique and several state-of-arttechniques described in the literature. The simulation results revealed that theAFCEDP technique demonstrates good capability in contrast enhancement anddetail preservation. In addition, the experiments using cervical cell images andHEp-2 cell images showed great potential of the AFCEDP technique as atechnique for enhancing medical microscopic images

Image Compensation Techniques.

Image clarity is very easily affected by lighting, weather, or equipment that has been used to capture the image

Investigation and Assessment of Disorder of Ultrasound B-mode Images

Digital image plays a vital role in the early detection of cancers, such as prostate cancer, breast cancer, lungs cancer, cervical cancer. Ultrasound imaging method is also suitable for early detection of the abnormality of fetus. The accurate detection of region of interest in ultrasound image is crucial. Since the result of reflection, refraction and deflection of ultrasound waves from different types of tissues with different acoustic impedance. Usually, the contrast in ultrasound image is very low and weak edges make the image difficult to identify the fetus region in the ultrasound image. So the analysis of ultrasound image is more challenging one. We try to develop a new algorithmic approach to solve the problem of non clarity and find disorder of it. Generally there is no common enhancement approach for noise reduction. This paper proposes different filtering techniques based on statistical methods for the removal of various noise. The quality of the enhanced images is measured by the statistical quantity measures: Signal-to-Noise Ratio (SNR), Peak Signal-to-Noise Ratio (PSNR), and Root Mean Square Error (RMSE).Comment: Pages IEEE format, International Journal of Computer Science and Information Security, IJCSIS, Vol. 7 No. 2, February 2010, USA. ISSN 1947 5500, http://sites.google.com/site/ijcsis