Deblurring of noisy iris images in iris recognition

Abstract

Iris recognition used the iris features to verify and identify the identity of human. The iris has many advantages such as stability over time, easy to use and high recognition accuracy. However, the poor quality of iris images can degrade the recognition accuracy of iris recognition system. The recognition accuracy of this system is depended on the iris pattern quality captured during the iris acquisition. The iris pattern quality can degrade due to the blurry image. Blurry image happened due to the movement during image acquisition and poor camera resolution. Due to that, a deblurring method based on the Wiener filter was proposed to improve the quality of iris pattern. This work is significant since the proposed method can enhance the quality of iris pattern in the blurry image. Based to the results, the proposed method improved the quality of iris pattern in the blurry image. Moreover, it recorded the fastest execution time to improve the quality of iris pattern compared to the other methods.

Image segmentation algorithm of lung cancer based on neural network model

“…Experimental process: image data collection, image pre‐processing, establishment of neural network algorithm model, train neural network, and accuracy verification of example experiments. Among them, image pre‐processing uses Wiener filtering to suppress image noise (Jamaludin et al, 2021; Park et al, 2020; Brown et al, 2019), and blur enhancement to improve image contrast (Li et al, 2020; Golestan et al, 2014; Cao et al, 2008; Li et al, 2011), and then extract texture and fractal features from the pre‐processed image, and finally input artificial model. The segmentation algorithm function was used to train the image neural network model (Saha et al, 2011; Kemnitz & Eckstein, 2017).…”

Enhanced Iris Recognition Method by Generative Adversarial Network-Based Image Reconstruction

“…Liu et al proposed deblurring method to automatically enhance the quality of both defocused and motion blurred iris images [59]. In [60], authors proposed the method of deblurring of noisy images in iris recognition.…”

Improving the iterative back projection estimation through Lorentzian sharp infinite symmetrical filter

“…12, No. 3, June 2022: 2539-2552 2540 existence of several limitation factors in the real acquisition system such as constraints that occurred in the point spread function (PSF) of the camera that contains information of blur, translation, and decimation factors [34]. In addition, some constraints on the image acquisition may lead to the presence of global noise that contaminated the acquired image [21]- [23], [26], [29]- [33].…”

“…The re-construction process takes sub-pixel shifted information from the sequence of LR images or sensor parameters provided by the image registration task. Indeed, the accuracy of sub-pixel motion is a very important factor for SR reconstruction solution to produce a finer image [21], [23], [27], [31], [34], [36]- [47]. Then, the interpolation task is taking place to align the nonuniform space of the LR image onto a uniformly HR image grid.…”