Image enhancement is the preprocessing task in digital image processing. It helps to improve the appearance or perception of the image so that the image can be used for analytics and human visual system. Image enhancement techniques lie in three broad categories—spatial domain, frequency domain, and fuzzy domain-based enhancement. A lot of work has been done on image enhancement. Most of the work has been done/performed on grayscale image. This paper concentrates on image enhancement using fuzzy logic approach and gives an insight into previous research work and future perspectives.

Image Enhancement Using Fuzzy Logic Techniques

In computer-based search systems, similarity plays a key role in replicating the human search process. Indeed, the human search process underlies many natural abilities such as image recovery, language comprehension, decision making, or pattern recognition. The search for images consists of establishing a correspondence between the available image and that sought by the user, by measuring the similarity between the images. Image search by content is generaly based on the similarity of the visual characteristics of the images. The distance function used to evaluate the similarity between images depends notonly on the criteria of the search but also on the representation of the characteristics of the image. This is the main idea of a content-based image retrieval (CBIR) system. In this article, first, we constructed type-2 beta fuzzy membership of descriptor vectors to help manage inaccuracy and uncertainty of characteristics extracted the feature of images. Subsequently, the retrieved images are ranked according to the novel similarity measure, noted type-2 fuzzy nearness measure (IT2FNM). By analogy to Type-2 Fuzzy Logic and motivated by near sets theory, we advanced a new fuzzy similarity measure (FSM) noted interval type-2 fuzzy nearness measure (IT-2 FNM). Then, we proposed three new IT-2 FSMs and we have provided mathematical justification to demonstrate that the proposed FSMs satisfy proximity properties (i.e. reflexivity, transitivity, symmetry, and overlapping). Experimental results generated using three image databases showing consistent and significant results.

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Interval Type-2 Beta Fuzzy Near Sets Approach to Content-Based Image Retrieval

In the fingertip blood automatic sampling process, when the blood sampling point in the fingertip venous area, it will greatly increase the amount of bleeding without being squeezed. In order to accurately locate the blood sampling point in the venous area, we propose a new finger vein image segmentation approach basing on Gabor transform and Gaussian mixed model (GMM). Firstly, Gabor filter parameter can be set adaptively according to the differential excitation of image and we use the local binary pattern (LBP) to fuse the same-scale and multi-orientation Gabor features of the image. Then, finger vein image segmentation is achieved by Gabor-GMM system and optimized by the max flow min cut method which is based on the relative entropy of the foreground and the background. Finally, the blood sampling point can be localized with corner detection. The experimental results show that the proposed approach has significant performance in segmenting finger vein images which the average accuracy of segmentation images reach 91.6%.

Research on Finger Vein Image Segmentation and Blood Sampling Point Location in Automatic Blood Collection

Vein contraction and venous compression typically caused by low temperature and excessive placement pressure can blur the captured finger vein images and severely impair the quality of extracted features. To improve the quality of captured finger vein image, this paper proposes a 26-layer generator network constrained by Neighbors-based Binary Patterns (NBP) texture loss to recover the clear image (guessing the original clear image). Firstly, by analyzing various types and degrees of blurred finger vein images captured in real application scenarios, a method to mathematically model the local and global blurriness using a pair of defocused and mean blur kernels is proposed. By iteratively and alternatively convoluting clear images with both kernels in a multi-scale window, a polymorphic blur training set is constructed for network training. Then, NBP texture loss is used for training the generator to enhance the deblurring ability of the network on images. Lastly, a novel network structure is proposed to retain more vein texture feature information, and two residual connections are added on both sides of the residual module of the 26-layer generator network to prevent degradation and overfitting. Theoretical analysis and simulation results show that the proposed neighbors-based binary-GAN (NB-GAN) can achieve better deblurring performance than the the-state-of-the-art approaches. 

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Finger Vein Image Deblurring Using Neighbors-Based Binary-GAN (NB-GAN)

A deep journey into image enhancement: A survey of current and emerging trends

As a latest biometric technique, finger vein recognition has been proved to be one of the most reliable biometric technology for personal authentication and security. The uncertainties and ambiguity in finger vein extraction can be handled more efficiently using fuzzy theory based techniques. Mamdani based fuzzy inference system is presented in this paper for locating the vein patterns in a finger vein image. A comparative analysis of the results is performed with some of the standard techniques.

Fuzzy inference system for finger vein biometric images

In this paper a unique finger vein image enhancement method is proposed using interval type-2 fuzzy sets. Image enhancement is a significant stage in finger vein recognition as it is necessary to minimize the effects of non-uniform illumination and low local contrast in the captured vein images. Interval type-2 fuzzy sets is presented as an efficient tool to enhance the finger vein images in the paper. Aggregate of the upper and lower membership functions of the interval type-2 fuzzy sets are is calculated using Einstein T co-norm. A significantly enhanced image is obtained with the modified membership function. The experiment is performed on different finger vein images and the results are compared with the existing methods.

Finger vein image enhancement using interval type-2 fuzzy sets

Fuzzy Techniques for Content-Based Image Retrieval

Finger vein recognition is explored extensively in the modern era as a competent biometric. The uncertainties and ambiguities in finger vein image retrieval can be handled more efficiently using fuzzy-theory-based techniques. A fuzzy set theoretic image retrieval is presented in this chapter for retrieving finger vein images. After assigning fuzzy membership values to grey-level histogram of images, the images are retrieved based on a fuzzy similarity measure using Einstein T-norm from the database. Evaluation of the performance of the system is executed using precision rates and recall rates.

P. Rose Bindu Joseph

Papers

Fuzzy inference system for finger vein biometric images

Finger vein image enhancement using interval type-2 fuzzy sets

Fuzzy Techniques for Content-Based Image Retrieval

Fuzzy Set Theoretic Similarity Measure for Finger Vein Image Retrieval