Brightness Preserving Dynamic Fuzzy Histogram Equalization Research Articles

Preprocessing of leaf images using brightness preserving dynamic fuzzy histogram equalization technique

Agriculture serves as the backbone of many countries. It provides food and other essential materials as per our requirement. Various kinds of diseases are affecting the agricultural crops which in turn reduce the quantity and quality of the agricultural sector. This can also lead to the decrease in food production thereby affecting the economic growth and development. Even though the symptoms and other impacts of the diseases are outwardly visible, manual identification of diseases and rectification is a tedious and time-consuming process. Therefore, detecting the diseases using an automatic computer-based model will be an effective solution. Image processing methods in conjunction with machine learning algorithms provide greater assistance in the field of plant disease detection. In the proposed work, plant leaf images of 10 crops are collected as the dataset. The images after acquisition are preprocessed using brightness preserving dynamic fuzzy histogram equalization (BPDFHE), an advanced version of histogram equalization and Gaussian filtering. The results are calculated and compared using the parameters such as peak signal to noise ratio (PSNR), structural similarity index (SSIM) and mean square error (MSE). This method performs more accurately than the existing preprocessing approaches.

Image enhancement in wavelet domain using histogram equalization and median filters

Image enhancement is one of the challenging but crucial methods that is employed in image processing technology for enhancing the visual appearance of images. This paper presents an effective and efficient image enhancement model in which Non Local mean (NLM) filter is used along with Minimum Mean Brightness Error Bi-Histogram Equalization (MMBEBHE) and Brightness Preserving Dynamic Fuzzy Histogram Equalization (BPDFHE) techniques. The primary objective of the proposed image enhancement model is to enhance the quality of images by reducing the noise effect in them. To do so, we have selected four different images of Barbara, camera, Lena and Hand whose quality is increased and analyzed on three different noise levels of 15, 20 and 25 respectively. Here, we have used NLM filter which is an advanced filtration technique for denoising the images. Also, MMBEBHE and BPDFHE techniques have been implemented for enhancing the quality of images on different noise levels. The efficiency and usefulness of proposed image enhancement model is examined and validated in MATLAB software under Mean Square Error (MSE) and Peak to Signal Ratio (PSNR) values.

New Contrast Enhancement Method for Multiple Sclerosis Lesion Detection.

Multiple sclerosis (MS) is oneof the most serious neurological diseases. It is the most frequent reason of non-traumatic disability among young adults. MS is an autoimmune disease wherein the central nervous system wrongly destructs the myelin sheath surrounding and protecting axons of nerve cells of the brain and the spinal cord which results in presence of lesions called plaques. The damage of myelin sheath alters the normal transmission of nerve flow at the plaqueslevel, consequently, a loss of communication between the brain and other organs. The consequence of this poor transmission of nerve impulses is the occurrence of various neurological symptoms. MS lesions cause mobility, vision, cognitive, and memory disorders. Indeed, early detection of lesions provides an accurate MS diagnosis. Consequently, and with the adequate treatment, clinicians will be able to deal effectively with the disease and reduce the number of relapses. Therefore, the use of magnetic resonance imaging (MRI) is primordial which is proven as the relevant imaging tool for early diagnosis of MS patients. But, low contrast MRI images can hide important objects in the image such lesions. In this paper, we propose a new automated contrast enhancement (CE) method to ameliorate the low contrast of MRI images for a better enhancement of MS lesions. This step is very important as it helps radiologists in confirming their diagnosis. The developed algorithm called BDS is based on Brightness Preserving Dynamic Fuzzy Histogram Equalization (BPDFHE) and Singular Value Decomposition with Discrete Wavelet Transform (SVD-DWT) techniques. BDS is dedicated to improve the low quality of MRI images with preservation of the brightness level and the edge details from degradation and without added artifacts or noise. These features are essential in CE approaches for a better lesion recognition. A modified version of BDS called MBDS is also implemented in the second part of this paper wherein we have proposed a new method for computing the correction factor. Indeed, with the use of the new correction factor, the entropy has been increased and the contrast is greatly enhanced. MBDS is specially dedicated for very low contrast MRI images.The experimental results proved the effectiveness of developed methods in improving low contrast of MRI images with preservation of brightness level and edge information. Moreover, performances of both proposed BDS and MBDS algorithms exceeded conventional CE methods.

Low contrast enhancement technique for color images using interval-valued intuitionistic fuzzy sets with contrast limited adaptive histogram equalization

This work introduces a program to enhance images taken in low-light. Fuzzy set theory is creating a significant shift in image processing. Interval-valued intuitionistic fuzzy sets (IVIFS) based on intuitionistic fuzzy sets constructed from fuzzy sets are used to enhance images taken in low-light. In the proposed method, first the given low-light image is fuzzified by normal fuzzification. Then the fuzzified image is converted to an interval-valued intuitionistic fuzzy image. This image will be proposed enhanced image after applying the contrast limited adaptive histogram equalization (CLAHE). The experimental results reveal that the proposed method gives better results when compared with other existing methods like histogram equalization (HE), CLAHE, brightness preserving dynamic fuzzy histogram equalization (BPDFHE), histogram specification approach (HSA). Based on the performance analysis like entropy and correlation coefficient (CC), the proposed method gives better results.

Extraction of Multiple Diseases in Apple Leaf Using Machine Learning

This paper proposes a novel algorithm of segmentation of diseased part in apple leaf images. In agriculture-based image processing, leaf diseases segmentation is the main processing task for region of interest extraction. It is also extremely important to segment the plant leaf from the background in case on live images. Automated segmentation of plant leaves from the background is a common challenge in the processing of plant images. Although numerous methods have been proposed, still it is tough to segment the diseased part of the leaf from the live leaf images accurately by one particular method. In the proposed work, leaves of apple having different background have been segmented. Firstly, the leaves have been enhanced by using Brightness-Preserving Dynamic Fuzzy Histogram Equalization technique and then the extraction of diseased apple leaf part is done using a novel extraction algorithm. Real-time plant leaf database is used to validate the proposed approach. The results of the proposed novel methodology give better results when compared to existing segmentation algorithms. From the segmented apple leaves, color and texture features are extracted which are further classified as marsonina coronaria or apple scab using different machine learning classifiers. Best accuracy of 96.4% is achieved using K nearest neighbor classifier.

RETRACTED ARTICLE: Different classification methods of fundus image segmentation using quincunx wavelet decomposition

Retinal vessel in fundus image is segmented with a comprehensive method for classification. The method is processed in four phases namely, preprocessing, segmentation, features extraction and classification, this method can be used on different images sets. Retinal vessels are enhanced by brightness preserving dynamic fuzzy histogram equalization (BPDFHE), separating enhanced image is used to detect the retinal diseases. Then these enhanced images are segmented by using quincunx wavelet decomposition for extracting features like first order statistics and gray level co-occurrence matrix (GLCM). The feature vector encodes information to handle the normal and abnormal retinal image and those features are classified using different classifiers (Adaboost, DSVM, ELMASR, EPLS, KNN, NB, NBFFS, OCPLS, RBFN, RF, SOWA, SVM and SVNN) and the performance is evaluated in detail. Blood vessel segmentation with this method is effective for retinal image computational analyses such as early retinal disease detection. Experimental results on three public retinal data sets like DRIVE, STARE and MESSIDOR and real time images are taken from Agarwal’s Eye Hospital, Tirunelveli, demonstrating an excellent performance in comparison with retinal vessel segmentation methods reported recently.

Haze Removal Algorithm for Optical Remote Sensing Image Based on Multi-Scale Model and Histogram Characteristic

Optical imaging remote sensing technology is an important technical means to obtain information of ground objects, but it is restricted by bad weather such as clouds, rain, and haze. In haze weather condition, optical images often have poor contrast and blurred details, which has a great impact on subsequent applications and interpretation. If the acquired images are processed, not only their quality can be improved, but also their visual effect and utilization value can be improved, so as to reduce the impact brought by haze. In general, the haze is removed from the view of image processing. In this paper, through in-depth analysis and study of existing algorithms and characteristics of optical remote sensing images, a new idea is proposed to solve this problem from the view of combination of image content and auxiliary information. Furthermore, a new haze removal algorithm is proposed based on the Retinex multi-scale model and the histogram characteristics of remote sensing images. Because the new method combines multi-scale model (MSM) and histogram characteristics (HC), it is referred to as MSMHC algorithm in this paper. The advantage of the new method is that the content and type of image are considered in the whole process, and then two processing schemes are set for haze removal. In the test experiments, one hundred groups of image data were used to carry out comparative experiments. At the same time, single-scale Retinex (SSR) algorithm, multi-scale Retinex (MSR) algorithm, dark channel priori (DCP) method, brightness preserving dynamic fuzzy histogram equalization (BPDFHE) algorithm, histogram equalization (HE) method, and homomorphic filter (HF) algorithm were used for comparative experiments with the MSMHC method. Five parameters, including standard deviation (SD), information entropy (IE), peak signal to noise ratio (PSNR), structural similarity (SSIM), and image contrast (IC), were used to quantitatively evaluate the test results. The experimental results and the parameter values showed that the MSMHC algorithm could not only effectively remove haze from remote sensing images, obtain high contrast and high definition images, but also have better generalization ability.

Quality enhancement of infrared images using dynamic fuzzy histogram equalization and high pass adaptation in DWT

Recently, Infrared (IR) images have several applications in many fields, especially in the fields of medicine and military. This paper presents an efficient approach for enhancing the contrast, appearance, and quality of IR images. The proposed approach works by applying a brightness preserving dynamic fuzzy Histogram Equalization (BPDFHE) and High Pass Adaptation (HPA) function to the different frequency bands outcome after performing the Discrete Wavelet Transform (DWT) on IR image to be enhanced. The coarse approximation LL band is enhanced by employing BPDFHE. The HPA is applied to the horizontal, vertical, and diagonal detail components represented by HL, LH, and HH bands, respectively. The proposed approach is tested and compared with traditional IR enhancement techniques. Experimental tests ensure the superiority and efficiency of the proposed approach in enhancing contrast and sharpening the edges of the low contrast IR images.

A New Approach of Cup to Disk Ratio Based Glaucoma Detection Using Fundus Images

This paper introduces a procedure for glaucoma exposure by computing cup to disk ratio for low contrast digital fundus images. The designated approach is based on pre-processing, processing and post processing steps. Pre-processing consists of enhancing contrast of optic cup using brightness preser ving dynamic fuzzy histogram equalization (BPDFHE) and optic disk regions, the processing step involves the segmentation of optic cup and optic disk and post processing will finally measure cup to disk ratio of given fundus image to detect glaucoma in the image. The method was successful in 381 images out of 403, achieving 98.20% sensitivity, 96.70% specificity and 97.67% global accuracy in pre diagnosis.

Face Detection and Tracking in Fuzzy Enhanced Low Contrast Images

Face detection and tracking in images have become an important concept for the researchers in the field of Digital Image Processing. Image enhancement refers to the improvement of inner details of image for visual perception. This paper combines these two concepts, using fuzzy logic based enhancement techniques to detect face in low contrast images. After fuzzy enhancement of the input image next step is detection and segmentation of the skin region. Enhancement is based on Fuzzy Intensification Operator (FIO) and Brightness Preserving Dynamic Fuzzy Histogram Equalization (BPDFHE).The detected faces are tracked by enclosing them using two dimensional regions. Comparative study of the enhancement algorithms on face tracking is carried out on the parameters like quality factor, segmentation error, and overlap area.

Improving Brightness Using Dynamic Fuzzy Histogram Equalization

This paper proposed brightness preserving dynamic fuzzy histogram equalization using triangular membership function which is the modified technique of histogram equalization. This modified technique, called Brightness Preserving Dynamic Fuzzy Histogram Equalization (BPDFHE), uses fuzzy statistics of digital images for their representation and processing in the fuzzy area which enables the technique to handle the approximation of gray level values in a better way for better presentation. This algorithm enhances image contrast as well as conserves the brightness very well. Some images are not available to excellent quality, so proposed Fuzzy algorithm can be used for image enhancement to improve the quality of the image.

Brightness preserving dynamic fuzzy histogram equalization

This paper proposes a novel modification of the brightness preserving dynamic histogram equalization technique to improve its brightness preserving and contrast enhancement abilities while reducing its computational complexity. The modified technique, called Brightness Preserving Dynamic Fuzzy Histogram Equalization (BPDFHE), uses fuzzy statistics of digital images for their representation and processing. Representation and processing of images in the fuzzy domain enables the technique to handle the inexactness of gray level values in a better way, resulting in improved performance. Execution time is dependent on image size and nature of the histogram, however experimental results show it to be faster as compared to the techniques compared here. The performance analysis of the BPDFHE along with that for BPDHE has been given for comparative evaluation.