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The purpose of this paper is to find an efficient fabric defect detection algorithm by means of exploring the sparsity characteristics of main local binary pattern (MLBP) extracted from the original fabric texture.

In the proposed algorithm, original LBP features are extracted from the fabric texture to be detected, and MLBP are selected by occurrence probability. Second, a dictionary is established with MLBP atoms which can sparsely represent all the LBP. Then, the value of the gray-scale difference between gray level of neighborhood pixels and the central pixel, and the mean of the difference which has the same MLBP feature are calculated. And then, the defect-contained image is reconstructed as normal texture image. Finally, the residual is calculated between reconstructed and original images, and a simple threshold segmentation method can divide the residual image, and the defective region is detected.

The experiment result shows that the fabric texture can be more efficiently reconstructed, and the proposed method achieves better defect detection performance. Moreover, it offers empirical insights about how to exploit the sparsity of one certain feature, e.g. LBP.

Because of the selected research approach, the results may lack generalizability in chambray. Therefore, researchers are encouraged to test the proposed propositions further.

In this paper, a novel fabric defect detection method which extracts the sparsity of MLBP features is proposed.

Geometric errors are common in metallic bent tubular parts. Thus, tubes should be inspected and fixed before welding with the joints first. After welding, the relative position of the joints is also necessary to be inspected to judge whether the tube can be assembled reliably. Therefore, the inspection plays an important role in the tube’s assembly. The purpose of this paper is to propose a multi-vision-based system designed to inspect the tube and the relative position of the joints.

For the tube inspection, the small cylinders are taken as the primitives to reconstruct the tube using the multi- vision-based system. Then, any geometric error in the tube can be inspected by comparing the reconstructed models and designed ones. For joints’ inspection, authors designed an adapter with marked points, by which the system can calculate the relative position of the joints.

The reconstruction idea can recognise the line and arc segments of a tube automatically and resolve the textureless deficiency of the tube’s surface. The joints’ inspection method is simple in operation, and any kinds of joints can be inspected by designing the structure of the adapters accordingly.

By experimental verification, the inspection precision of the proposed system was 0.17 mm; the inspection time was within 2 min. Thus, the system developed can inspect a tube effectively and automatically. Moreover, authors can determine how the springback of the arcs behaves, allowing in-process springback prediction and compensation, which can reduce geometric errors in the tubes given the present bending machine accuracy.

The purpose of this paper is to introduce an improved method for joint training of low‐ and high‐resolution dictionaries in the single image super resolution. With simulations, the proposed method is thereafter evaluated.

Sparse representations of low‐resolution image patches are used to reconstruct the high‐resolution image patches with high resolution dictionary. By using different factors, the scheme weights the two dictionaries in the high‐ and low‐resolution spaces in the training process. It is reasonable to achieve better reconstructed images with more emphasis on the high‐resolution spaces.

An improved joint training algorithm based on K‐SVD is developed with flexible weight factors on dictionaries in the high‐ and low‐resolution spaces. From the experiment results, the proposed scheme outperforms the classic bicubic interpolation and neighbor‐embedding learning based method.

By using flexible weight factors in joint training of the dictionaries for super resolution, better reconstruction results can be achieved.

Images taken from construction site interiors often suffer from low illumination and poor natural colors, which restrict their application for high-level site management purposes. The state-of-the-art low-light image enhancement method provides promising image enhancement results. However, they generally require a longer execution time to complete the enhancement. This study aims to develop a refined image enhancement approach to improve execution efficiency and performance accuracy.

To develop the refined illumination enhancement algorithm named enhanced illumination quality (EIQ), a quadratic expression was first added to the initial illumination map. Subsequently, an adjusted weight matrix was added to improve the smoothness of the illumination map. A coordinated descent optimization algorithm was then applied to minimize the processing time. Gamma correction was also applied to further enhance the illumination map. Finally, a frame comparing and averaging method was used to identify interior site progress.

The proposed refined approach took around 4.36–4.52 s to achieve the expected results while outperforming the current low-light image enhancement method. EIQ demonstrated a lower lightness-order error and provided higher object resolution in enhanced images. EIQ also has a higher structural similarity index and peak-signal-to-noise ratio, which indicated better image reconstruction performance.

The proposed approach provides an alternative to shorten the execution time, improve equalization of the illumination map and provide a better image reconstruction. The approach could be applied to low-light video enhancement tasks and other dark or poor jobsite images for object detection processes.

The paper aims to present an innovative solution for evaluation study of the dampness level of walls and historical buildings.

Electrical tomography enables one to obtain a distribution pattern of wall dampness. The application of modern tomographic techniques in conjunction with topological algorithms will allow one to perform very accurate spatial assessment of the dampness levels of buildings. The proposed application uses the total variation, Gauss–Newton and level set method to solve the inverse problem in electrical tomography.

Research shows that electrical tomography can provide effective results in damp buildings. This method can provide 2D/3D moisture distribution pattern.

The impact of this technique will be limited to inspection of the facility after floods or assessment of historical buildings.

The presented method could eventually lead to a much more effective evaluation of moisture in the walls.

The solution has commercial potential and could result in more cost-effective monitoring of historical buildings, which have an economic impact on society.

The authors propose a system for imaging spatial moistness of walls and historic buildings based on electrical tomography and consisting of a measuring device, sensors and image reconstruction algorithms.

The problems of Super resolution are broadly discussed in diverse fields. Rather than the progression toward the super resolution models for real-time images, operating hyperspectral images still remains a challenging problem.

This paper aims to develop the enhanced image super-resolution model using “optimized Non-negative Structured Sparse Representation (NSSR), Adaptive Discrete Wavelet Transform (ADWT), and Optimized Deep Convolutional Neural Network”. Once after converting the HR images into LR images, the NSSR images are generated by the optimized NSSR. Then the ADWT is used for generating the subbands of both NSSR and HRSB images. The residual image with this information is obtained by the optimized Deep CNN. All the improvements on the algorithms are done by the Opposition-based Barnacles Mating Optimization (O-BMO), with the objective of attaining the multi-objective function concerning the “Peak Signal-to-Noise Ratio (PSNR), and Structural similarity (SSIM) index”. Extensive analysis on benchmark hyperspectral image datasets shows that the proposed model achieves superior performance over typical other existing super-resolution models.

From the analysis, the overall analysis of the suggested and the conventional super resolution models relies that the PSNR of the improved O-BMO-(NSSR+DWT+CNN) was 38.8% better than bicubic, 11% better than NSSR, 16.7% better than DWT+CNN, 1.3% better than NSSR+DWT+CNN, and 0.5% better than NSSR+FF-SHO-(DWT+CNN). Hence, it has been confirmed that the developed O-BMO-(NSSR+DWT+CNN) is performing well in converting LR images to HR images.

This paper adopts a latest optimization algorithm called O-BMO with optimized Non-negative Structured Sparse Representation (NSSR), Adaptive Discrete Wavelet Transform (ADWT) and Optimized Deep Convolutional Neural Network for developing the enhanced image super-resolution model. This is the first work that uses O-BMO-based Deep CNN for image super-resolution model enhancement.

The purpose of this study is to evaluate the intra- and post-operative performance and safety of direct three dimensional printing (3DP) porous polyethylene implants in cranial reconstruction.

Prefabricated porous polyethylene implants were prepared by direct 3DP, and cranioplasty implantation was performed. Postoperative aesthetics, patient satisfaction, firmness of the implant, reactions to the implant and 3D computed tomography (CT) scanning were assessed after 2, 6, 12 and 24 months postoperatively.

No complications after surgery were encountered. Excellent aesthetic results were obtained in all cases, and all the patients were satisfied with the reconstruction outcome. Bone density structure was found to ingrowth into these direct 3DP porous polyethylene implants and the content increased with increasing follow-up times.

This study was a pilot study conducted in a single group and evaluated in a short-term period. The bone formation and ingrowth were indirectly assessed by 3D CT evaluation.

This work reported the use and evaluation of direct 3DP porous polyethylene in middle- to large-sized cranial reconstructions. It evidently showed the bonding of implants to surrounding tissues which would result in the long-term stability and infection resistance of the implant.

There are demands from the industry to have a modern application of Electrical Capacitance Tomography (ECT) system which is mobile and agile. One of the factors why such system is needed in the industry is because of the requirement to install the measurement sensors in a hostile and harsh environment which demands a special kind of ECT system. This paper will discuss the features of mobile or portable ECT which is more practical to be implemented in the harsh environment. Besides, the implementation of cloud computing and wireless technology in the portable ECT systems is also discussed. This review outlines some key features of portable or in another word mobile ECT as a complete system.

There are demands from the industry to have a modern application of ECT system which is mobile and agile. One of the factors why such system is needed in the industry is due to the requirement to install the measurement sensors in hostile and harsh environment which demands a special kind of ECT systems. This paper will discuss the features of mobile or portable ECT which is more practical to be implemented in the harsh environment. Besides, the implementation of cloud computing and wireless technology in the portable ECT systems is also being discussed. This review outlines some key features of portable or in another word mobile ECT as a complete system.

This review outlines some key features of portable or in another word mobile ECT as a complete system. A lot of improvement can be done to realize a reliable and stable ECT system. It is seems that in the near future, machine to machine communication will become the main stream.

This paper fulfils an identified need to study improvement that can be done to develop a portable ECT system which is reliable and stable. Besides, the implementation of cloud computing and wireless technology in the portable ECT systems is also discussed.

A major key success factor regarding proficient Bayes threshold denoising refers to noise variance estimation. This paper focuses on assessing different noise variance estimations in three Bayes threshold models on two different characteristic brain lesions/tumor magnetic resonance imaging (MRIs).

Here, three Bayes threshold denoising models based on different noise variance estimations under the stationary wavelet transforms (SWT) domain are mainly assessed, compared to state-of-the-art non-local means (NLMs). Each of those three models, namely D1, GB and DR models, respectively, depends on the most detail wavelet subband at the first resolution level, on the entirely global detail subbands and on the detail subband in each direction/resolution. Explicit and implicit denoising performance are consecutively assessed by threshold denoising and segmentation identification results.

Implicit performance assessment points the first–second best accuracy, 0.9181 and 0.9048 Dice similarity coefficient (Dice), sequentially yielded by GB and DR; reliability is indicated by 45.66% Dice dropping of DR, compared against 53.38, 61.03 and 35.48% of D1 GB and NLMs, when increasing 0.2 to 0.9 noise level on brain lesions MRI. For brain tumor MRI under 0.2 noise level, it denotes the best accuracy of 0.9592 Dice, resulted by DR; however, 8.09% Dice dropping of DR, relative to 6.72%, 8.85 and 39.36% of D1, GB and NLMs is denoted. The lowest explicit and implicit denoising performances of NLMs are obviously pointed.

A future improvement of denoising performance possibly refers to creating a semi-supervised denoising conjunction model. Such model utilizes the denoised MRIs, resulted by DR and D1 thresholding model as uncorrupted image version along with the noisy MRIs, representing corrupted version ones during autoencoder training phase, to reconstruct the original clean image.

This paper should be of interest to readers in the areas of technologies of computing and information science, including data science and applications, computational health informatics, especially applied as a decision support tool for medical image processing.

In most cases, DR and D1 provide the first–second best implicit performances in terms of accuracy and reliability on both simulated, low-detail small-size region-of-interest (ROI) brain lesions and realistic, high-detail large-size ROI brain tumor MRIs.

In the shadow of X‐ray tomography and nuclear magnetic resonance tomography a new tomographic technique based on low‐frequency electric currents has been successfully developed in the past decade. Impedance computed tomography (ICT), although it gives poor spatial resolution images, is unmatched in certain cases. Two different approaches to the sensitivity analysis are presented in this paper. The direct differentiation method has been applied, but the adjoint variable method has not been used in ICT reconstruction algorithms until now. Some problems associated with the adjoint variable method of sensitivity analysis are discussed. The new algorithm is compared with the direct differentiation approach.