Blur is a key determinant in the perception of image quality. Generally, blur causes spread of edges, which leads to shape changes in images. Discrete orthogonal moments have been widely studied as effective shape descriptors. Intuitively, blur can be represented using discrete moments since noticeable blur affects the magnitudes of moments of an image. With this consideration, this paper presents a blind image blur evaluation algorithm based on discrete Tchebichef moments. The gradient of a blurred image is first computed to account for the shape, which is more effective for blur representation. Then the gradient image is divided into equal-size blocks and the Tchebichef moments are calculated to characterize image shape. The energy of a block is computed as the sum of squared non-DC moment values. Finally, the proposed image blur score is defined as the variance-normalized moment energy, which is computed with the guidance of a visual saliency model to adapt to the characteristic of human visual system. The performance of the proposed method is evaluated on four public image quality databases. The experimental results demonstrate that our method can produce blur scores highly consistent with subjective evaluations. It also outperforms the state-of-the-art image blur metrics and several general-purpose no-reference quality metrics.

In the recent MRI scanning, ultra-high-field (7T) MR imaging provides higher resolution and better tissue contrast compared to routine 3T MRI, which may help in more accurate and early brain diseases diagnosis. However, currently, 7T MRI scanners are more expensive and less available at clinical and research centers. These motivate us to propose a method for the reconstruction of images close to the quality of 7T MRI, called 7T-like images, from 3T MRI, to improve the quality in terms of resolution and contrast. By doing so, the post-processing tasks, such as tissue segmentation, can be done more accurately and brain tissues details can be seen with higher resolution and contrast. To do this, we have acquired a unique dataset which includes paired 3T and 7T images scanned from same subjects, and then propose a hierarchical reconstruction based on group sparsity in a novel multi-level Canonical Correlation Analysis (CCA) space, to improve the quality of 3T MR image to be 7T-like MRI. First, overlapping patches are extracted from the input 3T MR image. Then, by extracting the most similar patches from all the aligned 3T and 7T images in the training set, the paired 3T and 7T dictionaries are constructed for each patch. It is worth noting that, for the training, we use pairs of 3T and 7T MR images from each training subject. Then, we propose multi-level CCA to map the paired 3T and 7T patch sets to a common space to increase their correlations. In such space, each input 3T MRI patch is sparsely represented by the 3T dictionary and then the obtained sparse coefficients are used together with the corresponding 7T dictionary to reconstruct the 7T-like patch. Also, to have the structural consistency between adjacent patches, the group sparsity is employed. This reconstruction is performed with changing patch sizes in a hierarchical framework. Experiments have been done using 13 subjects with both 3T and 7T MR images. The results show that our method outperforms previous methods and is able to recover better structural details. Also, to place our proposed method in a medical application context, we evaluated the influence of post-processing methods such as brain tissue segmentation on the reconstructed 7T-like MR images. Results show that our 7T-like images lead to higher accuracy in segmentation of white matter (WM), gray matter (GM), cerebrospinal fluid (CSF), and skull, compared to segmentation of 3T MR images.

State-of-the-art sharpness assessment methods are mostly based on edge width, gradient, high-frequency energy, or pixel intensity variation. Such methods consider very little the image content variation in conjunction with the sharpness assessment which causes the sharpness metric to be less effective for different content images. In this paper, we propose an efficient no-reference image sharpness assessment called content aware total variation (CATV) by considering the importance of image content variation in sharpness measurement. By parameterizing the image TV statistics using generalized Gaussian distribution, the sharpness measure is identified by the standard deviation, and the image content variation evaluator is indicated by the shape parameter. However, the standard deviation is content-dependent which is different for the regions with strong edges, high frequency textures, low frequency textures, and blank areas. By incorporating the shape-parameter in moderating of the standard deviation, we propose a content aware sharpness metric. The experimental results show that the proposed method is highly correlated with the human vision system and has better sharpness assessment results than the state-of-the-art techniques on the blurred subset images of LIVE, TID2008, CSIQ, and IVC databases. Also, our method has very low computational complexity which is suitable for online applications. The correlations with the subjective of the four databases and statistical significance analysis reveal that our method has superior results when compared with previous techniques.

In a spliced blurred image, the spliced region and the original image may have different blur types. Splicing localization in this image is challenging when a forger uses image resizing as anti-forensics to remove the splicing traces anomalies. In this paper, we overcome this problem by proposing a method for splicing localization based on partial blur type inconsistency. In this method, after the block-based image partitioning, a local blur type detection feature is extracted from the estimated local blur kernels. The image blocks are classified into out-of-focus or motion blur based on this feature to generate invariant blur type regions. Finally a fine splicing localization is applied to increase the precision of regions boundary. We can use the blur type differences of the regions to trace the inconsistency for the splicing localization. Our experimental results show the efficiency of the proposed method in the detection and the classification of the out-of-focus and motion blur types.

In a tampered blurred image generated by splicing, the spliced region and the original image may have different blur types. Splicing localization in this image is a challenging problem when a forger uses some postprocessing operations as antiforensics to remove the splicing traces anomalies by resizing the tampered image or blurring the spliced region boundary. Such operations remove the artifacts that make detection of splicing difficult. In this paper, we overcome this problem by proposing a novel framework for blurred image splicing localization based on the partial blur type inconsistency. In this framework, after the block-based image partitioning, a local blur type detection feature is extracted from the estimated local blur kernels. The image blocks are classified into out-of-focus or motion blur based on this feature to generate invariant blur type regions. Finally, a fine splicing localization is applied to increase the precision of regions boundary. We can use the blur type differences of the regions to trace the inconsistency for the splicing localization. Our experimental results show the efficiency of the proposed method in the detection and the classification of the out-of-focus and motion blur types. For splicing localization, the result demonstrates that our method works well in detecting the inconsistency in the partial blur types of the tampered images. However, our method can be applied to blurred images only.

In this paper, we propose a novel method for image tampering detection in multi-type blurred images. After block-based image partitioning, a space-variant prior for local blur kernels is proposed for local blur kernels estimation. Then, the image blocks are clustered using a k-means clustering based on the similarity of local blur kernels to generate blur type invariant regions. Finally, blur types of the regions are classified into out-of-focus or motion blur using a minimum distance classifier. The experimental results show that the proposed method successfully detects and classifies the regions blur types which outperforms the state-of-the-art techniques. Our proposed approach is used to detect inconsistency in the partial blur types of an image as an evidence of image tampering.

This letter proposes a simple and fast approach for no-reference image sharpness quality assessment. In this proposal, we define the maximum local variation (MLV) of each pixel as the maximum intensity variation of the pixel with respect to its 8-neighbors. The MLV distribution of the pixels is an indicative of sharpness. We use standard deviation of the MLV distribution as a feature to measure sharpness. Since high variations in the pixel intensities is a better indicator of the sharpness than low variations, the MLV of the pixels are subjected to a weighting scheme in such a way that heavier weights are assigned to greater MLVs to make the tail end of MLV distribution thicker. The weighting leads to an improvement of the MLV distribution to be more discriminative for different blur degrees. Finally, the standard deviation of the weighted MLV distribution is used as a metric to measure sharpness. The proposed approach has a very low computational complexity and the performance analysis shows that our approach outperforms the state-of-the-art techniques in terms of correlation with human vision system on several commonly used databases.

This paper proposes a novel approach for partial blur detection and segmentation. The local blur kernels of image blocks are firstly estimated and then a reblurring technique is used to measure relative blur degrees of the local blur kernels. The output of reblurring is a metric to classify blurred and non-blurred image blocks. Furthermore, block-based and pixel-based techniques are incorporated for a fine segmentation of blurred and non-blurred regions. Our approach is evaluated for out-of-focus and motion blurred images. The experimental results show that the proposed approach detects and segments the blurred and non-blurred regions in partial blurred images with 88% accuracy for natural out-of-focus blur, 86% accuracy for artificial out-of-focus blur and 83% accuracy for artificial motion blur, which outperforms the state-of-the-art approaches of partial blur detection and segmentation.

This paper presents an analytical model of the 3-tiered e-learning system on power line network based on Web services. Power line communication has high influence and is high speed medium that can be utilized as platform for Web based applications such as e-learning. To achieve this idea, we designed a 3-tiered e-learning system on power line network based on Web services and SOAP technology. Data tier is a distributed learning object repository to provide learning contents. Business tier is implemented in MV/MV (63 kV/20 kV) electrical power substations and Presentation tier is implemented in MV/LV (20 kV/400 V) electrical power substations. In this paper, we present analytical model of 3-tiered e-learning on symmetric and asymmetric power line network based on LMS and LCMS services. For this purpose, we use network queuing model and mean value analysis to predict response time of system. We show that to improve the response time of e-learning, the Web and application servers should be selected based on topology and requests of customers to e-learning services.