Image enhancement using fuzzy intensity measure and adaptive clipping histogram equalization

Image enhancement aims at processing an input image so that the visual content of the output image is more pleasing or more useful for certain applications. Although histogram equalization is widely used in image enhancement due to its simplicity and effectiveness, it changes the mean brightness of the enhanced image and introduces a high level of noise and distortion. To address these problems, this paper proposes image enhancement using fuzzy intensity measure and adaptive clipping histogram equalization (FIMHE). FIMHE uses fuzzy intensity measure to first segment the histogram of the original image, and then clip the histogram adaptively in order to prevent excessive image enhancement. Experiments on the Berkeley database and CVF-UGR-Image database show that FIMHE outperforms state-of-the-art histogram equalization based methods

Enhancement of dronogram aid to visual interpretation of target objects via intuitionistic fuzzy hesitant sets

In this paper, we address the hesitant information in enhancement task often caused by differences in image contrast. Enhancement approaches generally use certain filters which generate artifacts or are unable to recover all the objects details in images. Typically, the contrast of an image quantifies a unique ratio between the amounts of black and white through a single pixel. However, contrast is better represented by a group of pix- els. We have proposed a novel image enhancement scheme based on intuitionistic hesi- tant fuzzy sets (IHFSs) for drone images (dronogram) to facilitate better interpretations of target objects. First, a given dronogram is divided into foreground and background areas based on an estimated threshold from which the proposed model measures the amount of black/white intensity levels. Next, we fuzzify both of them and determine the hesitant score indicated by the distance between the two areas for each point in the fuzzy plane. Finally, a hyperbolic operator is adopted for each membership grade to improve the pho- tographic quality leading to enhanced results via defuzzification. The proposed method is tested on a large drone image database. Results demonstrate better contrast enhancement, improved visual quality, and better recognition compared to the state-of-the-art methods.Web of Science500866

Adaptive Local Fuzzy Based Region Determination Image Enhancement Techniques For Non-Uniform Illumination And Low Contrast Images

Local contrast enhancement is an approach to improve the local visibility detail of an image by increasing the contrast in local regions. Recently, researchers have shown an interest in solving the issue of non-uniform illumination. However, most of these techniques divide the image into two parts only namely over-exposed and under-exposed regions and try to enhance the poor contrast in both regions using same approach. However, these methods are not robust and they are specifically designed to solve a specific problem at one time. This limitation has motivated this study to propose a new technique to solve the abovementioned problems. In the beginning, Adaptive Local Exposure Based Region Determination (ALEBRD) method is proposed to determine and divide the image into three regions namely under-exposed, over-exposed, and well-exposed regions. The results show that the proposed ALEBRD method produced better region determination performance than the other state-of-the-art methods. Based on the qualitative analysis, it could determine those three regions with high accuracy. After that, contrast of each region will be enhanced using a new local contrast enhancement technique called Adaptive Fuzzy Exposure Local Contrast Enhancement (AFELCE). The proposed AFELCE method is specifically designed to enhance the contrast of each region using different approaches. The proposed AFELCE technique successfully improves the contrast of 300 low-contrast and non-uniform illumination images, taken from three different databases namely standard, underwater, and microscopic human sperm images. The proposed AFELCE method qualitatively and quantitatively outperforms the state-of-the-art methods,. Qualitatively, the proposed AFELCE method has successfully enhanced the contrast of those images by producing more uniform illumination images with high contrast. Quantitatively, the proposed AFELCE method produces the highest average of Entropy (E), Measure of Enhancement (EME) and Universal Image Quality Index (UIQI) for the standard image database with values of 7.582, 42.75 and 0.94 respectively. The similar results obtained for the underwater database images, where it produces the highest average of E, EME and UIQI values with 7.124, 41.13 and 0.89 respectivley. While for the microscopic human sperm image database, it produces the highest values for E and EME with values of 7.602 and 42.51 respectively, and . This study is suitable to be applied to a real time applications. Based on the good results obtained for standard, underwater, and microscopic human sperm images, the developed system has high potential and suitable to be applied to a real time applications

Lesion boundary segmentation using level set methods

This paper addresses the issue of accurate lesion segmentation in retinal imagery, using level set methods and a novel stopping mechanism - an elementary features scheme. Specifically, the curve propagation is guided by a gradient map built using a combination of histogram equalization and robust statistics. The stopping mechanism uses elementary features gathered as the curve deforms over time, and then using a lesionness measure, defined herein, ’looks back in time’ to find the point at which the curve best fits the real object. We implement the level set using a fast upwind scheme and compare the proposed method against five other segmentation algorithms performed on 50 randomly selected images of exudates with a database of clinician marked-up boundaries as ground truth

Enhancement Of The Low Contrast Image Using Fuzzy Set Theory

This paper presents a fuzzy grayscale enhancement technique for low contrast image. The degradation of the low contrast image is mainly caused by the inadequate lighting during image capturing and thus eventually resulted in nonuniform illumination in the image

Edge Enhancement from Low-Light Image by Convolutional Neural Network and Sigmoid Function

Due to camera resolution or any lighting condition, captured image are generally over-exposed or under-exposed conditions. So, there is need of some enhancement techniques that improvise these artifacts from recorded pictures or images. So, the objective of image enhancement and adjustment techniques is to improve the quality and characteristics of an image. In general terms, the enhancement of image distorts the original numerical values of an image. Therefore, it is required to design such enhancement technique that do not compromise with the quality of the image. The optimization of the image extracts the characteristics of the image instead of restoring the degraded image. The improvement of the image involves the degraded image processing and the improvement of its visual aspect. A lot of research has been done to improve the image. Many research works have been done in this field. One among them is deep learning. Most of the existing contrast enhancement methods, adjust the tone curve to correct the contrast of an input image but doesn’t work efficiently due to limited amount of information contained in a single image. In this research, the CNN with edge adjustment is proposed. By applying CNN with Edge adjustment technique, the input low contrast images are capable to adapt according to high quality enhancement. The result analysis shows that the developed technique significantly advantages over existing methods

Application of Adaptive Filters in Processing of Solar Corona Images

Fotografování sluneční koróny patří mezi nejobtížnější úlohy astrofotografie a zároveň je jednou z klíčových metod pro studium koróny. Tato práce přináší ucelený souhrn metod pro pozorování sluneční koróny pomocí snímků. Práce obsahuje nutnou matematickou teorii, postup pro zpracování snímků a souhrn adaptivních filtrů pro vizualizaci koronálních struktur v digitálních obrazech. Dále přináší návrh nových metod určených především pro obrazy s vyšším obsahem šumu, než je běžné u obrazů bílé koróny pořízených během úplných zatmění Slunce, např. pro obrazy pořízené pomocí úzkopásmových filtrů. Fourier normalizing-radial-graded filter, který byl navržen v rámci této práce, je založen na aproximaci hodnot pixelů a jejich variability pomocí trigonometrických polynomů s využitím dalších vlastností obrazu.Solar corona photography counts among the most complicated tasks in astrophotography. It also plays a key role for research of the solar corona. This thesis brings an a complete overview of methods for imaging the solar corona. The thesis contains necessary methematical background, the sequence of steps for image processing, an overview of adaptive filters used for visualization of corona structures in digital images, and new methods are proposed, especially for images which contain more noise than it is typical for images of the white corona taken during total solar eclipses, e.g. images taken with narrow-band filters. The Fourier normalizing-radial-graded filter method that I proposed during my PhD study are based on approximation of pixel values and their variability with trigonometric polynomials using other properties of the image.

Enhancement and Segmentation of Low-Light Images Using Illumination Map Estimation based Level Set (IME-LS) Method

One of the most difficult aspects of image segmentation is that it cannot be successfully segmented if the image is dark or degraded. In this paper, proposes a method for the segmentation of low-quality and degraded images is put forth which is called Illumination Map Estimation based Level Set (IME-LS) Method. This proposed model has been classified into two parts: Firstly, we use an enhancement approach using illumination map approximation to enhance the input image. In this approach, Illumination map is constructed then refined. The refined illumination map undergoes an Augmented Lagrangian algorithm and then we use a sped-up solver for a considerably more efficient outcome. Secondly, Then the segmentation procedure begins once the image is enhanced. The enhanced image is segmented using level set bias method through Fuzzy clustering. In this method we employ the Fuzzy C-Means (FCM) algorithm to categorize data points into clusters. The fuzzy C-Means algorithm separates different entities in the image based on their varied intensities and sorts them into various clusters. The level set bias approach then tracks the variational boundaries of the image. We have designed the integrated algorithm in such a way that the image is classified or grouped into various clusters using a novel fuzzy Clustering algorithm and the variational boundaries of those clusters are tracked by employing the level set algorithm. In this paper, we further perform quantitative and comparative analysis of the suggested technique with respect to other segmentation techniques to illustrate the efficiency and flexibility of the suggested model