Jiangxi University of Finance and Economics

About: Jiangxi University of Finance and Economics is a education organization based out in Nanchang, China. It is known for research contribution in the topics: Fuzzy logic & China. The organization has 2865 authors who have published 3556 publications receiving 41567 citations.

No-Reference Quality Assessment for Multiply-Distorted Images in Gradient Domain

Abstract: In practice, images available to consumers usually undergo several stages of processing including acquisition, compression, transmission, and presentation, and each stage may introduce certain type of distortion. It is common that images are simultaneously distorted by multiple types of distortions. Most existing objective image quality assessment (IQA) methods have been designed to estimate perceived quality of images corrupted by a single image processing stage. In this letter, we propose a no-reference (NR) IQA method to predict the visual quality of multiply-distorted images based on structural degradation. In the proposed method, a novel structural feature is extracted as the gradient-weighted histogram of local binary pattern (LBP) calculated on the gradient map (GWH-GLBP), which is effective to describe the complex degradation pattern introduced by multiple distortions. Extensive experiments conducted on two public multiply-distorted image databases have demonstrated that the proposed GWH-GLBP metric compares favorably with existing full-reference and NR IQA methods in terms of high accordance with human subjective ratings.

Saliency-Based Defect Detection in Industrial Images by Using Phase Spectrum

Abstract: For computer vision-based inspection of electronic chips or dies in semiconductor production lines, we propose a new method to effectively and efficiently detect defects in images. Different from the traditional methods that compare the image of each test chip or die with the template image one by one, which are sensitive to misalignment between the test and template images, a collection of multiple test images are used as the input image for processing simultaneously in our method with two steps. The first step is to obtain salient regions of the whole collection of test images, and the second step is to evaluate local discrepancy between salient regions in test images and the corresponding regions in the defect-free template image. To be more specific, in the first step of our method, phase-only Fourier transform (POFT), which is computationally efficient for online applications in industry, is used for saliency detection. We provide the theoretical justification for POFT to be effective to attenuate the normal regions and amplify the defects in multiple test images, which are usually arranged in a matrix format in industrial practice. By comparing with four other popular methods, the proposed algorithm can efficiently accommodate small variations (inevitable in practice) in test chips or dies, such as the spatial misalignments and product variations. Experimental results on a large-scale database including 1073 images, 94 of which are defective, show that our method performs much better than the other methods in terms of precision, recall, and F-measure.