Exploration of Current Trend on Blur Detection Method Utilized in Digital Image Processing

Koik, Boon Tatt; Ibrahim, Haidi

doi:10.12720/jiii.1.3.143-147

“…The blurred image may influence the product's precision, such as orthophoto and digital surface model. Koik and Ibrahim (2013) classified the image blur detection methods into seven categories: (1) edge sharpness analysis, (2) depth of field, (3) blind de-convolution, (4) Bayes discriminant function, (5) non-reference block, (6) lowest directional frequency energy and (7) wavelet-based histogram. Additionally, sensor-assisted blur detection (Jiang et al, 2014) is another way to detect blurred images.…”

Section: Introductionmentioning

confidence: 99%

Integration of Image-Derived and Pos-Derived Features for Image Blur Detection

Teo¹,

Zhan²

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission I, ICWG I/VbKEY WORDS: Position and Orientation System (POS), Unmanned Aerial Vehicle (UAV), blur image detection, degree of motion blur ABSTRACT:The image quality plays an important role for Unmanned Aerial Vehicle (UAV)'s applications. The small fixed wings UAV is suffering from the image blur due to the crosswind and the turbulence. Position and Orientation System (POS), which provides the position and orientation information, is installed onto an UAV to enable acquisition of UAV trajectory. It can be used to calculate the positional and angular velocities when the camera shutter is open. This study proposes a POS-assisted method to detect the blur image. The major steps include feature extraction, blur image detection and verification. In feature extraction, this study extracts different features from images and POS. The image-derived features include mean and standard deviation of image gradient. For POS-derived features, we modify the traditional degree-of-linear-blur (b linear ) method to degree-of-motion-blur (b motion ) based on the collinear condition equations and POS parameters. Besides, POS parameters such as positional and angular velocities are also adopted as POS-derived features. In blur detection, this study uses Support Vector Machines (SVM) classifier and extracted features (i.e. image information, POS data, b linear and b motion ) to separate blur and sharp UAV images. The experiment utilizes SenseFly eBee UAV system. The number of image is 129. In blur image detection, we use the proposed degree-of-motion-blur and other image features to classify the blur image and sharp images. The classification result shows that the overall accuracy using image features is only 56%. The integration of image-derived and POS-derived features have improved the overall accuracy from 56% to 76% in blur detection. Besides, this study indicates that the performance of the proposed degree-of-motion-blur is better than the traditional degree-of-linear-blur.

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“…Blind methods do not have such knowledge. A different division is suggested by the authors of [47], who put them into seven groups, which are edge sharpness analysis, depth of field, blind de-convolution, Bayes discriminant function, non-reference block, lowest directional frequency energy and wavelet-based histogram. Apart from the aforementioned groups, there are also sensor-assisted blur detection [40,42,48,49] and the latest neural methods [50][51][52][53][54][55][56][57][58][59][60][61][62][63][64][65].…”

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confidence: 99%

A Novel Method for the Deblurring of Photogrammetric Images Using Conditional Generative Adversarial Networks

Burdziakowski

¹

2020

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The visual data acquisition from small unmanned aerial vehicles (UAVs) may encounter a situation in which blur appears on the images. Image blurring caused by camera motion during exposure significantly impacts the images interpretation quality and consequently the quality of photogrammetric products. On blurred images, it is difficult to visually locate ground control points, and the number of identified feature points decreases rapidly together with an increasing blur kernel. The nature of blur can be non-uniform, which makes it hard to forecast for traditional deblurring methods. Due to the above, the author of this publication concluded that the neural methods developed in recent years were able to eliminate blur on UAV images with an unpredictable or highly variable blur nature. In this research, a new, rapid method based on generative adversarial networks (GANs) was applied for deblurring. A data set for neural network training was developed based on real aerial images collected over the last few years. More than 20 full sets of photogrammetric products were developed, including point clouds, orthoimages and digital surface models. The sets were generated from both blurred and deblurred images using the presented method. The results presented in the publication show that the method for improving blurred photo quality significantly contributed to an improvement in the general quality of typical photogrammetric products. The geometric accuracy of the products generated from deblurred photos was maintained despite the rising blur kernel. The quality of textures and input photos was increased. This research proves that the developed method based on neural networks can be used for deblur, even in highly blurred images, and it significantly increases the final geometric quality of the photogrammetric products. In practical cases, it will be possible to implement an additional feature in the photogrammetric software, which will eliminate unwanted blur and allow one to use almost all blurred images in the modelling process.

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Integration of Image-Derived and Pos-Derived Features for Image Blur Detection

Teo

¹

,

Zhao

²

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission I, ICWG I/VbKEY WORDS: Position and Orientation System (POS), Unmanned Aerial Vehicle (UAV), blur image detection, degree of motion blur ABSTRACT:The image quality plays an important role for Unmanned Aerial Vehicle (UAV)'s applications. The small fixed wings UAV is suffering from the image blur due to the crosswind and the turbulence. Position and Orientation System (POS), which provides the position and orientation information, is installed onto an UAV to enable acquisition of UAV trajectory. It can be used to calculate the positional and angular velocities when the camera shutter is open. This study proposes a POS-assisted method to detect the blur image. The major steps include feature extraction, blur image detection and verification. In feature extraction, this study extracts different features from images and POS. The image-derived features include mean and standard deviation of image gradient. For POS-derived features, we modify the traditional degree-of-linear-blur (b linear ) method to degree-of-motion-blur (b motion ) based on the collinear condition equations and POS parameters. Besides, POS parameters such as positional and angular velocities are also adopted as POS-derived features. In blur detection, this study uses Support Vector Machines (SVM) classifier and extracted features (i.e. image information, POS data, b linear and b motion ) to separate blur and sharp UAV images. The experiment utilizes SenseFly eBee UAV system. The number of image is 129. In blur image detection, we use the proposed degree-of-motion-blur and other image features to classify the blur image and sharp images. The classification result shows that the overall accuracy using image features is only 56%. The integration of image-derived and POS-derived features have improved the overall accuracy from 56% to 76% in blur detection. Besides, this study indicates that the performance of the proposed degree-of-motion-blur is better than the traditional degree-of-linear-blur.

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Exploration of Current Trend on Blur Detection Method Utilized in Digital Image Processing

Cited by 3 publications

References 25 publications

Integration of Image-Derived and Pos-Derived Features for Image Blur Detection

Integration of Image-Derived and Pos-Derived Features for Image Blur Detection

A Novel Method for the Deblurring of Photogrammetric Images Using Conditional Generative Adversarial Networks

Integration of Image-Derived and Pos-Derived Features for Image Blur Detection

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