Gradient Magnitude Map Research Articles

Learning sparse feature representation for blind quality assessment of night-time images

Capturing Night-Time Images (NTIs) with high-quality is quite challenging for consumer photography and several practical applications. Thus, addressing the quality assessment of night-time images is urgently needed. Since there is no available reference image for such images, Night-Time image Quality Assessment (NTQA) should be done blindly. Although Blind natural Image Quality Assessment (BIQA) has attracted a great deal of attention for a long time, very little work has been done in the field of NTQA. Due to the capturing conditions, NTIs suffer from various complex authentic distortions that make it a challenging field of research. Therefore, previous BIQA methods, do not provide sufficient correlation with subjective scores in the case of NTIs and special methods of NTQA should be developed. In this paper we conduct an unsupervised feature learning method for blind quality assessment of night-time images. The features are the sparse representation over the data-adaptive dictionaries learned on the image exposure and gradient magnitude maps. Having these features, an ensemble regression model trained using least squares gradient boosting scheme predicts high correlated objective scores on the standard datasets.

An automated technique for global noise level measurement in CT image with a conjunction of image gradient.

Automated assessment of noise level in clinical computed tomography (CT) images is a crucial technique for evaluating and ensuring the quality of these images. There are various factors that can impact CT image noise, such as statistical noise, electronic noise, structure noise, texture noise, artifact noise, etc. In this study, a method was developed to measure the global noise index (GNI) in clinical CT scans due to the fluctuation of x-ray quanta. Initially, a noise map is generated by sliding a 10 × 10 pixel for calculating Hounsfield unit (HU) standard deviation and the noise map is further combined with the gradient magnitude map. By employing Boolean operation, pixels with high gradients are excluded from the noise histogram generated with the noise map. By comparing the shape of the noise histogram from this method with Christianson's tissue-type global noise measurement algorithm, it was observed that the noise histogram computed in anthropomorphic phantoms had a similar shape with a close GNI value. In patient CT images, excluding the HU deviation due the structure change demonstrated to have consistent GNI values across the entire CT scan range with high heterogeneous tissue compared to the GNI values using Christianson's tissue-type method. The proposed GNI was evaluated in phantom scans and was found to be capable of comparing scan protocols between different scanners. The variation of GNI when using different reconstruction kernels in clinical CT images demonstrated a similar relationship between noise level and kernel sharpness as observed in uniform phantom: sharper kernel resulted in noisier images. This indicated that GNI was a suitable index for estimating the noise level in clinical CT images with either a smooth or grainy appearance. The study's results suggested that the algorithm can be effectively utilized to screen the noise level for a better CT image quality control.

Direct feature extraction and image co-registration of morphological structure from Bayer pattern raw planetary images

Feature extraction from planetary remote sensing images is a primary image processing task for object recognition, crater counter, morphological structure dimension and unpaired image co-registration. The paper presents a novel methodology to extract features directly from Bayer Pattern raw planetary images. Gradient information is extracted from Bayer Pattern image using standard edge operator that follows Color Difference Constancy (CDC) assumption. The proposed method's advantage is that it can skip the computationally intensive image processing pipeline and can have the Bayer Pattern raw planetary image flow directly for useful information extraction. Sobel Edge Detector is applied on Indian Mars Color Camera (MCC) Bayer Pattern raw images, and Gradient Magnitude Map (GMM) is generated at different Martian terrains. In addition, we have developed a direct image co-registration approach for MCC Bayer intensity raw image with respect to Mars Digital Image Model (MDIM) 2.1 reference using Mode-Mean Combo Patch Filler and Gradient Intensity induced Scale Invariant Feature Transform (GI-SIFT) based feature matching. The outlier matched points are removed by Feature Similarity Score guided Random Sample Consensus (FSS-RANSAC) estimation technique. The visual evaluation and quantitative metrics indicate that GMM from MCC Bayer Pattern raw image has negligible degradation with respect to GMM extracted from MCC demosaic image. The Root Mean Square Error (RMSE) is computed at different Mars regions, and it is found that the average image co-registration accuracy is less than 0.5 pixel.

Unsupervised blind image quality assessment based on joint structure and natural scene statistics features

Compared with the widely used supervised blind image quality assessment (BIQA) models, unsupervised BIQA models require little prior knowledge for calculating the objective quality scores of distorted images. In this paper, we propose an unsupervised BIQA method that aims to achieve both good performance and generalization capability with low computational complexity. Carefully selected and extensive structure and natural scene statistics (NSS) features can better represent image quality. First, we employ phase congruency (PC) and finely selected gradient magnitude map and Laplacian of Gaussian response (GM-LOG) features to represent image structure information. Second, we calculate the local mean-subtracted and contrast-normalized (MSCN) coefficients and the Karhunen–Loéve transform (KLT) coefficients to represent the naturalness of the distorted images. Last, multivariate Gaussian (MVG) model with joint features extracted from both the pristine images and the distorted images is adopted to calculate the objective image quality. Extensive experiments conducted on nine IQA databases demonstrate that the proposed method achieves better performance than the state-of-the-art BIQA methods.

A fast face recognition based on image gradient compensation for feature description

To improve the efficiency of traditional face recognition techniques, this paper proposes a novel face recognition algorithm called Image Gradient Feature Compensation (IGFC). Based on the gradients along four directions in an image, a fusion algorithm and a compensation method are implemented to obtain features of the original image. In this study, gradient magnitude maps of a face image are calculated along four directions. Fusion gradients and differential fusion gradients are produced by fusing the four gradient magnitude maps of a face image in multiple ways, and they are used as compensation variables to compensate the appropriate coefficients on the original image and build IGFC feature maps of the original face image. Subsequently, IGFC feature maps are divided into several blocks to calculate the concatenated histogram over all blocks, which is in turn utilized as the feature descriptor for face recognition. Principal component analysis (PCA) is used to cut down the number of dimensions in high-dimensional features, which are recognized by the Support Vector Machine (SVM) classifier. Finally, the proposed IGFC method is superior to traditional methods as suggested by verification studies on YALE, ORL, CMU_PIE, and FERET face databases. When the LibSVM parameter was set to ‘-s 0 -t 2 -c 16 -g 0.0009765625’, the algorithm achieved 100% recognition on Yale and ORL data sets, 92.16% on CMU_PIE data sets, and 74.3% on FERET data sets. The average time for simultaneous completion of the data sets examined was 1.93 s, and the algorithm demonstrated a 70.71% higher running efficiency than the CLBP algorithm. Therefore, the proposed algorithm is highly efficient in feature recognition with excellent computational efficiency.

A novel approach for evaluating bone mineral density of hips based on Sobel gradient-based map of radiographs utilizing convolutional neural network

Osteoporosis, which is a common disorder associated with low bone mineral density (BMD), is one of the primary reasons for hip fracture. It not only limits mobility, but also makes the patient suffer from pain. Unlike traditional methods, which require both expensive equipment and long scanning times, this study aims to develop a novel technique employing a convolutional neural network (CNN) directly on radiographs of the hips to evaluate BMD. To construct the dataset, X-ray photographs of lower limbs and dual-energy X-ray absorptiometry (DXA) results of the hips of patients were collected. The core of this research is a deep learning-based model that was trained using the pre-processed X-rays images of 510 hips as the input data and the BMD values obtained from DXA as the standard reference. To improve performance quality, the radiographs of the hips were processed with a Sobel algorithm to extract the gradient magnitude maps, and an ensemble artificial neural network which analyses the outputs of CNN models corresponding to three Singh sites and biological parameters was utilized. The superior performance of the proposed method was confirmed by the high correlation coefficient of 0.8075 (p<0.0001) of the BMD measured by DXA in a total of 150 testing cases, with only 0.12 s required for applying the computing configuration to a single X-ray image.

Blind image quality assessment in the contourlet domain

No-reference/blind image quality assessment (NR-IQA/BIQA) algorithms play an important role in image evaluation, as they can assess the quality of an image automatically, only using the distorted image whose quality is being assessed. Among the existing NR-IQA/BIQA methods, natural scene statistic (NSS) models which can be expressed in different bandpass domains show good consistency with human subjective judgments of quality.In this paper, we create new ‘quality-aware’ features: the energy differences of the sub-band coefficients across scales via contourlet transform, and propose a new NR-IQA/BIQA model that operates on natural scene statistics in the contourlet domain. Prior to applying the contourlet transform, we apply two preprocessing steps that help to create more information-dense, low-entropy representations. Specifically, we transform the picture into the CIELAB color space and gradient magnitude map. Then, a number of ‘quality-aware’ features are discovered in the contourlet transform domain: the energy of the sub-band coefficients within scales, and the energy differences between scales, as well as measurements of the statistical relationships of pixels across scales. A detailed analysis is conducted to show how different distortions affect the statistical characteristics of these features, and then features are fed to a support vector regression (SVR) model which learns to predict image quality. Experimental results show that the proposed method has high linearity against human subjective perception, and outperforms the state-of-the-art NR-IQA models.

Texture Classification Using Pair-wise Difference Pooling Based Bilinear Convolutional Neural Networks.

Texture is normally represented by aggregating local features based on the assumption of spatial homogeneity. Effective texture features are always the research focus even though both hand-crafted and deep learning approaches have been extensively investigated. Motivated by the success of Bilinear Convolutional Neural Networks (BCNNs) in fine-grained image recognition, we propose to incorporate the BCNN with the Pair-wise Difference Pooling (i.e. BCNN-PDP) for texture classification. The BCNN-PDP is built on top of a set of feature maps extracted at a convolutional layer of the pre-trained CNN. Compared with the outer product used by the original BCNN feature set, the pair-wise difference not only captures the pair-wise relationship between two sets of features but also encodes the difference between each pair of features. Considering the importance of the gradient data to the representation of image structures, we further generalise the BCNN-PDP feature set to two sets of feature maps computed from the original image and its gradient magnitude map respectively, i.e. the Fused BCNN-PDP (F-BCNN-PDP) feature set. In addition, the BCNN-PDP can be applied to two different CNNs and is referred to as the Asymmetric BCNN-PDP (A-BCNN-PDP). The three PDP-based BCNN feature sets can also be extracted at multiple scales. Since the dimensionality of the BCNN feature vectors is very high, we propose a new yet simple Block-wise PCA (BPCA) method in order to derive more compact feature vectors. The proposed methods are tested on seven different datasets along with 21 baseline feature sets. The results show that the proposed feature sets are superior, or at least comparable, to their counterparts across different datasets.

Visual Security Evaluation of Perceptually Encrypted Images Based on Image Importance

Perceptual/selective encryption has been gaining widespread attention as an emerging technology for image privacy protection. However, few studies focus on the visual security evaluation of perceptually encrypted images, which has a significant impact on measuring the effectiveness and practicality of these encryption methods. In this paper, we propose an image importance-based visual security index (IIBVSI) by leveraging spatial contrast and texture features. Based on the characteristics of perceptually encrypted images, we present an averaged high-order gradient magnitude map to describe the spatial contrast feature and introduce a combined local amplitude map of multiple log-Gabor filters to represent the texture feature. Specifically, the multiresolution representation of an image is first created by downsampling to simulate the hierarchical property of the human visual system. Next, for each scale of image resolution, the spatial contrast and the texture feature maps are extracted from both plain and encrypted images. Similarity measurements are then conducted on these feature maps to generate the contrast and the texture similarity maps. An image importance-based pooling strategy is subsequently proposed to combine these measurements and generate a visual security score. The final IIBVSI score is computed by averaging the visual security scores of all scales of image resolution. Extensive experiments are conducted on several publicly available databases, and the results demonstrate the superiority and robustness of our proposed IIBVSI compared with existing state-of-the-art work in the low and moderate image quality ranges.

Edge position detection and depth estimation from gravity data with application to mineral exploration

A method is applied to residual gravity data taken from the Ortaklar mine, Turkey to delineate the edge position and to estimate the depth of a buried ore body. The horizontal gradient magnitude (HGM) and tilt angle map (TAM) techniques are applied to the first vertical derivative (FVD) of the residual gravity data. The maxima contours of the HGM data and the zero contours of the TAM correspond to the edges of the ore body. The half distance between ± 45° (± 0.785 rad) TAM contours is used to determine the depths of the body. The obtained results are then compared against the depths retrieved from the Grav2dc inversion program, which inverted two profiles taken across the maximum response of the gravity anomaly map. The depths of the ore body estimated by the two methods are found in good agreement as reveal from drilling information.

No reference quality evaluation for screen content images considering texture feature based on sparse representation

Abstract In this paper, an accurate blind metric evaluating screen content images (SCIs) considering texture feature via sparse representation is proposed. The existing theory on human vision tells us that human visual system (HVS) is sensitive to texture information of images. In addition, gradient direction has not been adequately explored as a predictive information source of SCIs. This method is first based on three kinds of gradient maps of SCIs—i.e., gradient magnitude map, relative gradient direction map, relative gradient magnitude map. Since the existing literatures about human neuroscience have revealed that image texture can be captured by high-order derivatives, texture information of SCIs can be represented by local histogram of oriented gradient (HOG) features extracted from the aforementioned gradient maps. Finally, it is composed of two important procedures: representing HOG features by means of sparse coding, then weighting subjective experimental quality scores via the sparse coding coefficients to predict objective perceptive quality scores of SCIs. Comparison experiments on the public SCI database demonstrate that the designed metric has the better superiority over existing the-state-of-art algorithms and delivers consistency and accuracy in relation with subjective evaluation.

Estimating Depth-Salient Edges and Its Application to Stereoscopic Image Quality Assessment.

The human visual system pays attention to salient regions while perceiving an image. When viewing a stereoscopic 3-D (S3D) image, we hypothesize that while most of the contribution to saliency is provided by the 2-D image, a small but significant contribution is provided by the depth component. Further, we claim that only a subset of image edges contribute to depth perception while viewing an S3D image. In this paper, we propose a systematic approach for depth saliency estimation, called salient edges with respect to depth perception (SED) which localizes the depth-salient edges in an S3D image. We demonstrate the utility of SED in full reference stereoscopic image quality assessment. We consider gradient magnitude and inter-gradient maps for predicting structural similarity. A coarse quality map is estimated first by comparing the 2-D saliency and gradient maps of reference and test stereo pairs. We average this quality map to estimate luminance quality and refine this quality map using SED maps for evaluating depth quality. Finally, we combine this luminance and depth quality to obtain an overall stereo image quality. We perform a comprehensive evaluation of our metric on seven publicly available S3D IQA databases. The proposed metric shows competitive performance on all seven databases with state-of-the-art performance on three of them.

Referenceless quality metric of multiply-distorted images based on structural degradation

Abstract Multiply-distorted images, that is, distorted by different types of distortions simultaneously, are so common in real applications. This kind of images contain multiple overlaying stages (e.g., acquisition, compression and transmission stage). Each stage will introduce a certain type of distortion, for example, sensor noise in acquisition stage and compression artifacts in compression stage. However, most current blind/no-reference image quality assessment (NR-IQA) methods are specifically designed for singly-distorted images, thus resulting in their deficiency in handling multiply-distorted images. Motivated by the hypothesis that human visual system (HVS) is adapted to the structural information in images, we attempt to assess multiply-distorted images based on structural degradation. To this end, we use both first- and high-order image structures to design a novel referenceless quality metric for multiply-distorted images. Specifically, we leverage the quality-aware features extracted from both the gradient-magnitude map and contrast-normalized map, and further improve the performance by making use of redundancy of features with random subspace method. Experimental results on popular multiply-distorted image databases verify the outstanding performance of the proposed method.

Spatial analysis of ambient gamma dose equivalent rate data by means of digital image processing techniques

A detailed ambient gamma dose equivalent rate mapping based on field measurements at ground level and at 1 m height was carried out at 142 sites in 80 × 90 km area in Pest County, Hungary. Detailed digital image processing analysis was carried out to identify and characterise spatial features such as outlying points, anomalous zones and linear edges in a smoothed TIN interpolated surface. The applied method proceeds from the simple shaded relief model and digital cross-sections to the more complex gradient magnitude and gradient direction maps, 2nd derivative profile curvature map, relief map and lineament density map. Each map is analysed for statistical characteristics and histogram-based image segmentation is used to delineate areas homogeneous with respect to the parameter values in these maps. Assessment of spatial anisotropy is implemented by 2D autocorrelogram and directional variogram analyses. The identified spatial features are related to underlying geological and tectonic conditions using GIS technology. Results show that detailed digital image processing is efficient in revealing the pattern present in field-measured ambient gamma dose equivalent rates and they are related to regional scale tectonic zones and surface sedimentary lithological conditions in the study area.

Structural and geodynamic study in central Tunisia using field and geophysical data: new structural interpretation of the N–S axis and associated Atlassic structures

A structural and geodynamic evolution of central Tunisia is constructed from field studies and geophysical data within the Tunisian Atlassic domain. Bouguer gravity and horizontal gradient magnitude (HGM) maps indicate that computed anomalies are related to subsurface structures and are in accordance with the general structural trends of central Tunisia. Near Cherichira fault, the strike is ENE–WSW, whereas it is N–S along the N–S axis, a major discontinuity between the Pelagian shelf to the east and Central Atlas Mountains to the west. E–W-oriented HGM profiles on the north side of the Cherichira fault show moderate HGM variations, which correspond to the NE–SW fault system of the Ousselet–Bou Dabbous. This trend is also expressed by the second-order vertical derivative map of this area. However, on the south side of the fault, a dominant peak is indicated rather than the anomalies characteristic of the north side of the fault. This anomaly is associated with the N–S axis discontinuity. Seismic reflection data are also used to elucidate the structural configuration and style north and south of the Cherichira fault. The seismic lines demonstrate that faulting and associate halokinesis in Ousselet–Bou Dabbous area are not comparable to the paleogeographic fault system of the N–S axis southward. To the south of Cherichira fault, the seismic lines show massive Triassic evaporates piercing along the major paleogeographic faults associated with thickness variations of Mesozoic and Cenozoic stratigraphic units and a reverse slip of western structural elements. It seems that the N–S axis corresponds to a steeply dipping major zone of weakness in the Tunisian Atlassic Mountains and indicates evidence of several stages of activity, including the rise of Triassic evaporates rise up which outcrop at several localities such as Rheouis and Mezzouna. The loading and upward rise of Triassic evaporites along this weak zone are a key factor in the thinning of the Mesozoic–Cenozoic sedimentary cover along this paleogeographic discontinuity. These new data indicate the northeast extrapolation of fold and grabens of the Central Atlas of Tunisia to the northeast of Kairouan, whereas the northern extent of the N–S axis is limited to south of the Cherichira fault. The Cherichira fault is NE–SW oriented and parallels the Ballouta–Zaghouan fault system, which reflects the regional strike of the Tellian orogeny and the associated nappes to the north. The geometry and kinematics of these Tellian features are linked to the tectonic evolution of the northern African margin, including Mesozoic extension separating the African and European plates to the inversion of these structures since the Late Cretaceous. The N–S axis is correlated in its direction and tectonic framework to the strike-slip fault system of the African craton, which is present to the south in the Algerian Saharan domain of Hoggar.

Blind image quality assessment using joint statistics of gradient magnitude and Laplacian features.

Blind image quality assessment (BIQA) aims to evaluate the perceptual quality of a distorted image without information regarding its reference image. Existing BIQA models usually predict the image quality by analyzing the image statistics in some transformed domain, e.g., in the discrete cosine transform domain or wavelet domain. Though great progress has been made in recent years, BIQA is still a very challenging task due to the lack of a reference image. Considering that image local contrast features convey important structural information that is closely related to image perceptual quality, we propose a novel BIQA model that utilizes the joint statistics of two types of commonly used local contrast features: 1) the gradient magnitude (GM) map and 2) the Laplacian of Gaussian (LOG) response. We employ an adaptive procedure to jointly normalize the GM and LOG features, and show that the joint statistics of normalized GM and LOG features have desirable properties for the BIQA task. The proposed model is extensively evaluated on three large-scale benchmark databases, and shown to deliver highly competitive performance with state-of-the-art BIQA models, as well as with some well-known full reference image quality assessment models.

A Blind Blur Detection Scheme Using Statistical Features of Phase Congruency and Gradient Magnitude

The growing uses of camera-based barcode readers have recently gained a lot of attention. This has boosted interest in no-reference blur detection algorithms. Blur is an undesirable phenomenon which appears as one of the most frequent causes of image degradation. In this paper we present a new no-reference blur detection scheme that is based on the statistical features of phase congruency and gradient magnitude maps. Blur detection is achieved by approximating the functional relationship between these features using a feed forward neural network. Simulation results show that the proposed scheme gives robust blur detection scheme.

Formula omitted] Structured descriptive experiment design regularization based enhancement of fractional SAR imagery

Feature-enhanced reconstruction of the reflectivity maps (remotely sensed scene images) from the low-resolution fractional SAR imagery is treated for harsh sensing scenarios with uncertainties attributed to possible imperfect sensor calibration, atmospheric turbulence and uncontrolled carrier trajectory deviations. These effects lead to the randomly perturbed signal formation operator resulting in a partial coherence of the system. A low-resolution scene image formed using the conventional matched spatial filtering method serves as a starting point for the enhancement. We commence with the descriptive experiment design regularization (DEDR) approach for solving the image enhancement inverse problem based on the ℓ2 -type squared error norm minimization strategy robust against the problem model uncertainties in the sense of the worst case statistical performance optimization. To exploit structural information on the desired image piecewise smoothness over the scene the ℓ1 structured regularization level is incorporated via aggregating the image total variation (TV) minimization approach with DEDR. In the unified DEDR-TV framework, the image gradient magnitude map sparsity and the overall image texture anisotropy properties are structured by combining the ℓ2 image metric with the ℓ1 image gradient metric in the solution space. The incorporated projections onto convex solution sets guarantee the convergence and speed up the resulting implicit iterative enhancement scheme.

Segmentation of Medical Image using Clustering and Watershed Algorithms

Problem statement: Segmentation plays an important role in medical imaging. Segmentation of an image is the division or separation of the image into dissimilar regions of similar attribute. In this study we proposed a methodology that integrates clustering algorithm and marker controlled watershed segmentation algorithm for medical image segmentation. The use of the conservative watershed algorithm for medical image analysis is pervasive because of its advantages, such as always being able to construct an entire division of the image. On the other hand, its disadvantages include over segmentation and sensitivity to false edges. Approach: In this study we proposed a methodology that integrates K-Means clustering with marker controlled watershed segmentation algorithm and integrates Fuzzy C-Means clustering with marker controlled watershed segmentation algorithm separately for medical image segmentation. The Clustering algorithms are unsupervised learning algorithms, while the marker controlled watershed segmentation algorithm makes use of automated thresholding on the gradient magnitude map and post-segmentation merging on the initial partitions to reduce the number of false edges and over-segmentation. Results: In this study, we compared K-means clustering and marker controlled watershed algorithm with Fuzzy C-means clustering and marker controlled watershed algorithm. And also we showed that our proposed method produced segmentation maps which gave fewer partitions than the segmentation maps produced by the conservative watershed algorithm. Conclusion: Integration of K-means clustering with marker controlled watershed algorithm gave better segmentation than integration of Fuzzy C-means clustering with marker controlled watershed algorithm. By reducing the amount of over segmentation, we obtained a segmentation map which is more diplomats of the several anatomies in the medical images.

Masseter segmentation using an improved watershed algorithm with unsupervised classification

The watershed algorithm always produces a complete division of the image. However, it is susceptible to over-segmentation and sensitivity to false edges. In medical images this leads to unfavorable representations of the anatomy. We address these drawbacks by introducing automated thresholding and post-segmentation merging. The automated thresholding step is based on the histogram of the gradient magnitude map while post-segmentation merging is based on a criterion which measures the similarity in intensity values between two neighboring partitions. Our improved watershed algorithm is able to merge more than 90% of the initial partitions, which indicates that a large amount of over-segmentation has been reduced. To further improve the segmentation results, we make use of K -means clustering to provide an initial coarse segmentation of the highly textured image before the improved watershed algorithm is applied to it. When applied to the segmentation of the masseter from 60 magnetic resonance images of 10 subjects, the proposed algorithm achieved an overlap index ( κ ) of 90.6%, and was able to merge 98% of the initial partitions on average. The segmentation results are comparable to those obtained using the gradient vector flow snake.