Multidirectional Gradient Neighbourhood-Weighted Image Sharpness Evaluation Algorithm

Xingya Yan,Zhi Zhao,Jian Lei

doi:10.1155/2020/7864024

Abstract

Aiming at the problem that the image sharpness evaluation algorithm in the photoelectric system has a slow speed in actual processing and is severely disturbed by noise, an improved image sharpness evaluation algorithm is proposed by combining multiscale decomposition tools and multidirectional gradient neighbourhood weighting. This paper applies non-subsampled shearlet transform (NSST) to perform multiscale transformation of the input images, obtaining high-frequency sub-band images and low-frequency sub-band images. In order to enhance the detection of the edge orientation of images, multidirectional gradient processing of the image matrix is added to each sub-band image. In addition, the weight corresponding to the current pixel is obtained by calculating the inverse ratio of the gradient of each direction and the distance of the center pixel. Through calculating the ratio of the gradient neighbourhood weighting operators of high-frequency sub-band images and low-frequency sub-band images, the image sharpness evaluation value can be acquired further. Moreover, the image sequence collected by a certain type of photoelectric system is selected as the image sequence of the noisy real environment for simulation experiments and compared with the current mainstream algorithms. Finally, the experimental draws a conclusion that compared with the mainstream evaluation algorithms, the evaluation results of the proposed method perform better in terms of steepness, sensitivity, and flat area fluctuation, it can better suppress noise and improve accuracy, and its running speed meets the basic requirements of the image sharpness evaluation algorithm.

Highlights

IntroductionE sharpness evaluation function is a core part of autofocus technology
With the rapid development of digital image technology, as a core link of photoelectric systems, autofocus technology becomes an indispensable tool for obtaining clear images in a modern high-speed information society, which affects the image quality of imaging and the effectiveness of subsequent image algorithms [1,2,3].e sharpness evaluation function is a core part of autofocus technology
In order to verify the effectiveness of the proposed method for evaluating image sharpness, this paper used a certain type of photoelectric system to collect an image sequence as the image sequence which has a real noisy environment. e total number of frames of this image sequence is 461 frames, and the resolution is 640 × 512, as shown in Figure 4. e algorithms used for qualitative and quantitative analysis include gray variance evaluation algorithm, Brenner evaluation algorithm, Roberts evaluation algorithm, Prewitt evaluation algorithm, Laplace evaluation algorithm, Tenengrad evaluation algorithm, Vollath evaluation algorithm, entropy function evaluation algorithm, Fourier transform evaluation algorithm, discrete cosine transform (DCT) evaluation algorithm, and wavelet transform evaluation algorithm

Summary

Introduction

E sharpness evaluation function is a core part of autofocus technology. Spatial domain evaluation function makes use of images’ features in the spatial domain to distinguish blurred images and clear images. Because the function directly calculates the gray value of the image pixels, the amount of calculation is less and the calculation speed is faster [7,8,9]. Due to the fact that the edge and contour of an object is complex and changeable, the spatial evaluation function only detects the horizontal and vertical gradients of images, which cannot detect the complex edge contour accurately [10]. Zhang et al [12] proposed an improved Brenner sharpness evaluation function. Compared with the gray gradient evaluation function, this

Methods

Results

Conclusion