Multidimensional Contrast Limited Adaptive Histogram Equalization

Contrast enhancement is an important preprocessing technique for improving the performance of downstream tasks in image processing and computer vision. Among the existing approaches based on nonlinear histogram transformations, contrast limited adaptive histogram equalization (CLAHE) is a popular choice when dealing with 2D images obtained in natural and scientific settings. The recent hardware upgrade in data acquisition systems results in significant increase in data complexity, including their sizes and dimensions. Measurements of densely sampled data higher than three dimensions, usually composed of 3D data as a function of external parameters, are becoming commonplace in various applications in the natural sciences and engineering. The initial understanding of these complex multidimensional datasets often requires human intervention through visual examination, which may be hampered by the varying levels of contrast permeating through the dimensions. We show both qualitatively and quantitatively that using our multidimensional extension of CLAHE (MCLAHE) acting simultaneously on all dimensions of the datasets allows better visualization and discernment of multidimensional image features, as are demonstrated using cases from 4D photoemission spectroscopy and fluorescence microscopy. Our implementation of multidimensional CLAHE in Tensorflow is publicly accessible and supports parallelization with multiple CPUs and various other hardware accelerators, including GPUs

A Study on Geometry Contrast Enhancement for 3D Point Models

Electrical EngineeringPoint primitives have come into the spotlight as a representation method of 3D models. A lot of researches have been performed on the modeling, processing, and rendering 3D point models. Especially, various methods have been developed for the extraction and preservation of the salient features of corners, curves, and edges in 3D point models. However, little effort has been made to extract and enhance the weak features that are relatively imperceptible due to the low geometry contrast. In this thesis, we propose a novel method to improve the visibility of 3D point models by enhancing the geometry contrast of weak features. We first define a weak feature region as a group of local points yielding small deviations of normal directions. Then we define the geometry histogram for each region as the distribution of the signed distance between a feature point and the locally approximated plane. We equalize and stretch the geometry histogram and move the corresponding feature points accordingly. We also render the enhanced model using the normal mapping for better visual presentation. Experimental results demonstrate that the proposed method enhances the geometry contrast of 3D point models by refining the appearance of the weak features. We expect that the geometry contrast enhancement algorithm will facilitate many applications in various fields.ope

Color Histogram Equalization using Probability Smoothing

Publication in the conference proceedings of EUSIPCO, Florence, Italy, 200

Color Image Enhancement via Combine Homomorphic Ratio and Histogram Equalization Approaches: Using Underwater Images as Illustrative Examples

The histogram is one of the important characteristics of grayscale images, and the histogram equalization is effective method of image enhancement. When processing color images in models, such as the RGB model, the histogram equalization can be applied for each color component and, then, a new color image is composed from processed components. This is a traditional way of processing color images, which does not preserve the existent relation or correlation between colors at each pixel. In this work, a new model of color image enhancement is proposed, by preserving the ratios of colors at all pixels after processing the image. This model is described for the color histogram equalization (HE) and examples of application on color images are given. Our preliminary results show that the application of the model with the HE can be effectively used for enhancing color images, including underwater images. Intensive computer simulations show that for single underwater image enhancement, the presented method increases the image contrast and brightness and indicates a good natural appearance and relatively genuine color

Importance of Image Enhancement Techniques in Color Image Segmentation: A Comprehensive and Comparative Study

Color image segmentation is a very emerging research topic in the area of color image analysis and pattern recognition. Many state-of-the-art algorithms have been developed for this purpose. But, often the segmentation results of these algorithms seem to be suffering from miss-classifications and over-segmentation. The reasons behind these are the degradation of image quality during the acquisition, transmission and color space conversion. So, here arises the need of an efficient image enhancement technique which can remove the redundant pixels or noises from the color image before proceeding for final segmentation. In this paper, an effort has been made to study and analyze different image enhancement techniques and thereby finding out the better one for color image segmentation. Also, this comparative study is done on two well-known color spaces HSV and LAB separately to find out which color space supports segmentation task more efficiently with respect to those enhancement techniques.Comment: 27 pages, 17 figures, 2 Tables, 1 flowchar

Post-Processing of Low Dose Mammography Images

In mammography, X-ray radiation is used in sufficient doses to be captured on film for cancer diagnosis. A problem lies in the inherent nature of X-rays to cause cancer. The resolution of the images obtained on film is directly related to the radiation dosage. Thus, a trade-off between image quality and radiation exposure is necessary to ensure proper diagnosis without causing cancer. A possible solution is to decrease the dosage of radiation and improve the image quality of mammograms using post- processing methods applied to digitized film images. Image processing techniques that may improve the resolution of images captured at lower doses include crispening, denoising, histogram equalization, and pattern recognition methods. The Wright Patterson Air Force Base Hospital Radiology Department sponsored this research and provided digitized images of the American College of Radiology (ACR) phantom, which is a model for mammogram image quality and classification. Side by side comparisons were performed of high dose images and low-dose images post-processed using the methods mentioned. The result was improved- resolution on mammography images for lower radiation doses. Thus, this research represents progress towards solving a problem that currently plagues mammography: exposure of patients to high doses of cancer- causing radiation to obtain quality mammography images. By improving the image quality of mammography images at lower radiation doses, the problem of cancer induced by high radiation exposure is alleviated

Local Contrast Enhancement Utilizing Bidirectional Switching Equalization Of Separated And Clipped Sub-Histograms

Digital image contrast enhancement methods that are based on histogram equalization (HE) technique are useful for the use in consumer electronic products due to their simple implementation. However, almost all the suggested enhancement methods are using global processing technique, which does not emphasize local contents. Kaedah penyerlahan beza jelas imej digit berdasarkan teknik penyeragaman histogram adalah berguna dalam penggunaan produk elektronik pengguna disebabkan pelaksanaan yang mudah. Walau bagaimanapun, kebanyakan kaedah penyerlahan yang dicadangkan adalah menggunakan teknik proses sejagat dan tidak menekan kepada kandungan setempat