A novel approach to no-reference image quality assessment using canny magnitude based upon neural network

Kaur, Er. Amandeep; Wasson, Er. Vikas

doi:10.1109/rteict.2017.8256904

Cited by 1 publication

(2 citation statements)

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“…Although we are studying anomaly detection of satellite images, considering the anomalies as we define and illustrate above, the problem is more like image quality assessment (IQA). Many researchers have studied IQA, which can be divided into three types: full-reference IQA, reduced-reference IQA, and no-reference IQA (reference means reference images without distortions) [13]. Zhai and Min [14] provided an overview of classical algorithms and recent progresses in perceptual image quality assessment.…”

Section: Related Work and Novelties And Necessity Of The Studymentioning

confidence: 99%

“…Alaql et al [15] investigated the performance of different classification techniques and features to improve classification of distortions followed by IQA. Based on neural network, Kaur A et al proposed a novel no-reference IQA method using canny magnitude and achieved excellent results on LIVE and TID2008 datasets, proving the efficiency of ANN [13]. Besides, Wang [16] and Sebastian [17] et al adopted deep learning to IQA and achieved favorable results.…”

Section: Related Work and Novelties And Necessity Of The Studymentioning

confidence: 99%

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A Unified Framework for Anomaly Detection of Satellite Images Based on Well-Designed Features and an Artificial Neural Network

Wang

You

et al. 2021

Remote Sensing

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Nowadays, as the number of remote sensing satellites launched and applied in China has been mounting, relevant institutions’ workload of processing raw satellite images to be distributed to users is also growing. However, due to factors such as extreme atmospheric conditions, diversification of on-board device status, data loss during transmission and algorithm issues of ground systems, defect of image quality is inevitable, including abnormal color, color cast, data missing, obvious stitching line between Charge-Coupled Devices (CCDs), and inconstant radiation values between CCDs. Product application has also been impeded. This study presents a unified framework based on well-designed features an Artificial Neural Network (ANN) to automatically identify defective images. Samples were collected to form the dataset for training and validation, systematic experiments designed to verify the effectiveness of the features, and the optimal network architecture of ANN determined. Moreover, an effective method was proposed to explain the inference of ANN based on local gradient approximation. The recall of our final model reached 81.18% and F1 score 80.13%, verifying the effectiveness of our method.

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Section: Related Work and Novelties And Necessity Of The Studymentioning

confidence: 99%