Fundus images have been established as an important factor in analyzing and recognizing many cardiovascular and ophthalmological diseases. Consequently, precise segmentation of blood using computer vision is vital in the recognition of ailments. Although clinicians have adopted computer-aided diagnostics (CAD) in day-to-day diagnosis, it is still quite difficult to conduct fully automated analysis based exclusively on information contained in fundus images. In fundus image applications, one of the methods for conducting an automatic analysis is to ascertain symmetry/asymmetry details from corresponding areas of the retina and investigate their association with positive clinical findings. In the field of diabetic retinopathy, matched filters have been shown to be an established technique for vessel extraction. However, there is reduced efficiency in matched filters due to noisy images. In this work, a joint model of a fast guided filter and a matched filter is suggested for enhancing abnormal retinal images containing low vessel contrasts. Extracting all information from an image correctly is one of the important factors in the process of image enhancement. A guided filter has an excellent property in edge-preserving, but still tends to suffer from halo artifacts near the edges. Fast guided filtering is a technique that subsamples the filtering input image and the guidance image and calculates the local linear coefficients for upsampling. In short, the proposed technique applies a fast guided filter and a matched filter for attaining improved performance measures for vessel extraction. The recommended technique was assessed on DRIVE and CHASE_DB1 datasets and achieved accuracies of 0.9613 and 0.960, respectively, both of which are higher than the accuracy of the original matched filter and other suggested vessel segmentation algorithms.

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Guidance Image-Based Enhanced Matched Filter with Modified Thresholding for Blood Vessel Extraction

Text obtained in natural scenes contains various information; therefore, it is extensively used in various applications to understand the image scenarios and also to retrieve the visual information. The semantic information provided by this scene image is very much valuable for human beings to realize the whole environment. But the text in such natural images depicts a flexible appearance in an unconstrained environment which makes the text identification and character recognition process a more challenging one. Therefore, a weighted naive Bayes classifier (WNBC)-based deep learning process is used in this framework to effectively detect the text and to recognize the character from the scene images. Normally, the natural scene images may carry some kind of noise in it, and to remove that, the guided image filter is introduced at the pre-processing stage. The features that are useful for the classification process are extracted using the Gabor transform and stroke width transform techniques. Finally, with these extracted features, the text detection and character recognition is successfully achieved by WNBC and deep neural network-based adaptive galactic swarm optimization. Then, the performance metrics such as accuracy, F1-score, precision, mean absolute error, mean square error and recall metrics are evaluated to estimate the adeptness of the proposed method.

Hybrid deep neural network with adaptive galactic swarm optimization for text extraction from scene images

Guided image filtering in shape-from-focus: A comparative analysis

Infrared and visible image fusion based on weighted variance guided filter and image contrast enhancement

This paper presents a novel image contrast enhancement technique that uses exposure-based energy curve equalization (ECE) with a plateau limit. In a primary deviation from the current histogram equalization process for contrast enhancement, the proposed approach uses an energy curve for the same. The energy curve is computed based on the modified Hopfield neural network architecture, which contains spatial context information. The calculated energy curve is clipped with a plateau limit computed as the average of the energy curve. The exposure threshold is computed and used to divide the clipped energy curve. The two resulting energy curves are equalized independently, and the final enhanced image is generated by integrating the images achieved by transforming the equalized energy curves. The performance of the proposed method is evaluated on a variety of low contrast images. The subjective and objective evaluations of the proposed method are compared with the various histogram equalization (HE) based methods and other state-of-the-art methods to exemplify the effectiveness.

Exposure-Based Energy Curve Equalization for Enhancement of Contrast Distorted Images

Although the guided image filtering (GIF) has an excellent edge-preserving property, it is prone to suffer from the halo artifacts near the edges. Weighted GIF and gradient-domain GIF try to address the problem by incorporating an edge-aware weighting into GIF. However, they are very sensitive to the regularization parameter and the halo artifacts will become serious as the regularization parameter increases. Moreover, noise in the background is often amplified because of the fixed amplification factor for the detail layer. In this letter, an effective GIF is proposed for better contrast enhancement. First, the average of local variances for all pixels is incorporated into the cost function of GIF for preserving the edges accurately in the base layer. Second, the amplification factor for the detail layer is calculated in a content-adaptive way for suppressing the noise while boosting the fine details. Experimental results show that the proposed filter is more robust to the regularization parameter and can produce visually pleasing output images. Compared to GIF and its related filters, halo artifacts and noise are reduced or attenuated by the proposed filter significantly.

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Fajin Lu

Papers

Effective Guided Image Filtering for Contrast Enhancement