Restoration of images and 3D data to higher resolution by deconvolution with sparsity regularization

Image convolution is conventionally approximated by the LTI discrete model. It is well recognized that the higher the sampling rate, the better is the approximation. However sometimes images or 3D data are only available at a lower sampling rate due to physical constraints of the imaging system. In this paper, we model the under-sampled observation as the result of combining convolution and subsampling. Because the wavelet coefficients of piecewise smooth images tend to be sparse and well modelled by tree-like structures, we propose the L0 reweighted-L2 minimization (L0RL2 ) algorithm to solve this problem. This promotes model-based sparsity by minimizing the reweighted L2 norm, which approximates the L0 norm, and by enforcing a tree model over the weights. We test the algorithm on 3 examples: a simple ring, the cameraman image and a 3D microscope dataset; and show that good results can be obtained. © 2010 IEEE.

Identification of Materials from Construction Site Images Using Content Based Image Retrieval Techniques

The capability to automatically identify shapes, objects and materials from the image content through direct and indirect methodologies has enabled the development of several civil engineering related applications that assist in the design, construction and maintenance of construction projects. Examples include surface cracks detection, assessment of fire-damaged mortar, fatigue evaluation of asphalt mixes, aggregate shape measurements, velocimentry, vehicles detection, pore size distribution in geotextiles, damage detection and others. This capability is a product of the technological breakthroughs in the area of Image and Video Processing that has allowed for the development of a large number of digital imaging applications in all industries ranging from the well established medical diagnostic tools (magnetic resonance imaging, spectroscopy and nuclear medical imaging) to image searching mechanisms (image matching, content based image retrieval). Content based image retrieval techniques can also assist in the automated recognition of materials in construction site images and thus enable the development of reliable methods for image classification and retrieval. The amount of original imaging information produced yearly in the construction industry during the last decade has experienced a tremendous growth. Digital cameras and image databases are gradually replacing traditional photography while owners demand complete site photograph logs and engineers store thousands of images for each project to use in a number of construction management tasks. However, construction companies tend to store images without following any standardized indexing protocols, thus making the manual searching and retrieval a tedious and time-consuming effort. Alternatively, material and object identification techniques can be used for the development of automated, content based, construction site image retrieval methodology. These methods can utilize automatic material or object based indexing to remove the user from the time-consuming and tedious manual classification process. In this paper, a novel material identification methodology is presented. This method utilizes content based image retrieval concepts to match known material samples with material clusters within the image content. The results demonstrate the suitability of this methodology for construction site image retrieval purposes and reveal the capability of existing image processing technologies to accurately identify a wealth of materials from construction site images.

Comparison of manual and user guided methodologies for the classification and retrieval of construction site images

The amount of original imaging information produced yearly during the last decade has experienced a tremendous growth in all industries due to the technological breakthroughs in digital imaging and electronic storage capabilities. This trend is affecting the construction industry as well, where digital cameras and image databases are gradually replacing traditional photography. Owners demand complete site photograph logs and engineers store thousands of images for each project to use in a number of construction management tasks like monitoring an activity's progress and keeping evidence of the "as built" in case any disputes arise. So far, retrieval methodologies are done manually with the user being responsible for imaging classification according to specific rules that serve a limited number of construction management tasks. New methods that, with the guidance of the user, can automatically classify and retrieve construction site images are being developed and promise to remove the heavy burden of manually indexing images. In this paper, both the existing methods and a novel image retrieval method developed by the authors for the classification and retrieval of construction site images are described and compared. Specifically a number of examples are deployed in order to present their advantages and limitations. The results from this comparison demonstrates that the content based image retrieval method developed by the authors can reduce the overall time spent for the classification and retrieval of construction images while providing the user with the flexibility to retrieve images according different classification schemes.

Shape Recognition of Linear Construction Entities from Construction Site Images

The capability to automatically identify shapes, objects and materials from the image content through direct and indirect methodologies has enabled the development of several civil engineering related applications that assist in the design, construction and maintenance of construction projects. This capability is a product of the technological breakthroughs in the area of Image Processing that has allowed for the development of a large number of digital imaging applications in all industries. In this paper, an automated and content based shape recognition model is presented. This model was devised to enhance the recognition capabilities of our existing material based image retrieval model. The shape recognition model is based on clustering techniques, and specifically those related with material and object segmentation. The model detects the borders of each previously detected material depicted in the image, examines its linearity (length/width ratio) and detects its orientation (horizontal/vertical). The results emonstrate the suitability of this model for construction site image retrieval purposes and reveal the capability of existing clustering technologies to accurately identify the shape of a wealth of materials from construction site images.

Microfabrication of electrode patterns for high-frequency ultrasound transducer arrays.

High-frequency ultrasound is needed for medical imaging with high spatial resolution. A key issue in the development of ultrasound imaging arrays to operate at high frequencies (≥30 MHz) is the need for photolithographic patterning of array electrodes. To achieve this directly on 1-3 piezocomposite, the material requires not only planar, parallel, and smooth surfaces, but also an epoxy composite filler that is resistant to chemicals, heat, and vacuum. This paper reports, first, on the surface finishing of 1-3 piezocomposite materials by lapping and polishing. Excellent surface flatness has been obtained, with an average surface roughness of materials as low as 3 nm and step heights between ceramic/polymer of ∼80 nm. Subsequently, high-frequency array elements were patterned directly on top of these surfaces using a photolithography process. A 30-MHz linear array electrode pattern with 50-μm element pitch has been patterned on the lapped and polished surface of a high-frequency 1-3 piezocomposite. Excellent electrode edge definition and electrical contact to the composite were obtained. The composite has been lapped to a final thickness of ∼55 μm. Good adhesion of electrodes on the piezocomposite has been achieved and electrical impedance measurements have demonstrated their basic functionality. The array was then packaged, and acoustic pulse-echo measurements were performed. These results demonstrate that direct patterning of electrodes by photolithography on 1-3 piezocomposite is feasible for fabrication of high-frequency ultrasound arrays. Furthermore, this method is more conducive to mass production than other reported array fabrication techniques.

Control of material transport through pulse shape manipulation-a development toward designer pulses

The variety of laser systems available to industrial laser users is growing and the choice of the correct laser for a material target application is often based on an empirical assessment. Industrial master oscillator power amplifier systems with tuneable temporal pulse shapes have now entered the market, providing enormous pulse parameter flexibility in an already crowded parameter space. In this paper, an approach is developed to design interaction parameters based on observations of material responses. Energy and material transport mechanisms are studied using pulsed digital holography, post process analysis techniques and finite-difference modelling to understand the key response mechanisms for a variety of temporal pulse envelopes incident on a silicon (1/1/1) substrate. The temporal envelope is shown to be the primary control parameter of the source term that determines the subsequent material response and the resulting surface morphology. A double peak energy-bridged temporal pulse shape designed through direct application of holographic imaging data is shown to substantially improve surface quality. © 2014 IEEE.