Super Resolution of 3D Surface Texture

Research into texture synthesis is normally concerned with generation of 2D images of texture. However, real-world surface textures comprise rough surface geometry and various reflectance properties. These surface textures are different from 2D still texture as their images can therefore vary dramatically with illumination directions. This paper presents a simple framework for 3D surface texture synthesis. Firstly, we propose a novel 2D texture synthesis algorithm based on wavelet transform that can be efficiently extended to synthesise surface representations in multi-dimensional space. Then, photometric stereo (PS) is used to generate surface gradient and albedo maps from three synthesized surface texture images. The surface gradient maps can be further integrated to produce a surface height map (surface profile). With the albedo and height or gradient maps, new images of a Lambertian surface under arbitrary illuminant directions can be generated. Experiments show that the proposed approach can not only produce 3D surface textures under arbitrary illumination directions, but also has the ability to retain the surface geometry structure.

This paper introduces an improved graph cut approach and combines it with commonly used surface texture representations for synthesis. Unlike graph cut which supports iterative refinement for improvement of the patch seams, the improved graph cut method re-synthesis the max error overlap region using k-nearest neighbor method. This paper follows a simple 3D surface texture synthesis framework, which comprises three steps: capture, synthesis and relighting. Firstly, it represents 3D surface textures under varied illumination directions using gradient and albedo maps. Then, it uses the surface representation to synthesis a description of a larger surface. Finally, it renders the surface representation. It proposes an improved graph cut method for the synthesis step, and the proposed method can generate perceptually smooth images of 3D surface texture from small samples.

A sensor is proposed with interesting characteristics for applications such as autonomous navigation and virtual reality. The system extracts both 3D shape and surface texture. It is based on the projection of a regular pattern, observed by a single camera. It is cheap, requiring only a slide projector and a camera. Shape and intensity are extracted from one and the same image, thereby avoiding shape/texture alignment problems. Furthermore, the system is easy to use and calibrate. Because both the shape and the intensity information is obtained from a single image, the system can be used in dynamic environments, allowing robots to navigate during data acquisition or graphics designers to model moving objects for animation and virtual reality purposes. The system combines advantages of passive and active range acquisition.

Collecting pavement texture information is crucial to understand the characteristics of a road surface and to have essential data to support road maintenance. Traditional texture assessment techniques often require expensive equipment and complex operations. To ensure cost sustainability and reduce traffic closure times, this study proposes a rapid, cost-effective, and non-invasive surface texture assessment technique. This technology consists of capturing a set of images of a road surface with a mobile phone; then, the images are used to reconstruct the 3D surface with photogrammetric processing and derive the roughness parameters to assess the pavement texture. The results indicate that pavement images taken by a mobile phone can reconstruct the 3D surface and extract texture features with accuracy, meeting the requirements of a time-effective documentation. To validate the effectiveness of this technique, the surface structure of the pavement was analyzed in situ using a 3D structured light projection scanner and rigorous photogrammetry with a high-end reflex camera. The results demonstrated that increasing the point cloud density can enhance the detail level of the real surface 3D representation, but it leads to variations in road surface roughness parameters. Therefore, appropriate density should be chosen when performing three-dimensional reconstruction using mobile phone images. Mobile phone photogrammetry technology performs well in detecting shallow road surface textures but has certain limitations in capturing deeper textures. The texture parameters and the Abbott curve obtained using all three methods are comparable and fall within the same range of acceptability. This finding demonstrates the feasibility of using a mobile phone for pavement texture assessments with appropriate settings.

For the measurement of areal surface texture, a non-contact measurement method for areal surface texture based on the co-integration of optical microscopy image measurement and white light microscopy interference measurement is proposed, which measures not only 2D size of length but also 3D surface texture. Based on this method, a 2D&3D optical microscopy measurement system for areal surface texture has been developed. It consists of compound optical microscopy imaging system, 3D precision displacement system, hardware circuit, and corresponding software. The practicality and effectiveness of the proposed method of measurement have been verified by the measurement results.

Surface Texture for Geospatial Imaging Printed Product Using 3D Printer Technology

Vibration-assisted micro milling is a promising technique for fabricating engineered mi-cro-scaled surface textures. This paper presents a novel approach for theoretical modeling of three-dimensional (3D) surface textures produced by vibration-assisted micro milling. The proposed model considers the effects of tool edge geometry, minimum uncut chip thickness (MUCT), and material elastic recovery. The surface texture formation under different machining parameters is simulated and analyzed through mathematical modeling. Two typical surface morphologies can be generated: wave-type and fish scale-type textures, depending on the phase difference between tool paths. A 2-degrees-of-freedom (2-DOF) vibration stage is also developed to provide vibration along the feed and cross-feed directions during micro-milling process. Micro-milling experiments on copper were carried out to verify the ability to fabricate controlled surface textures using the vibration stage. The simulated and experimentally generated surfaces show good agreement in geometry and dimensions. This work provides an accurate analytical model for vibration-assisted micro-milling surface generation and demonstrates its feasibility for efficient, flexible texturing.

Investigation of the relationship between the white layer thickness and 3D surface texture parameters in the die sinking EDM process

Historical documents often undergo various changes over time, that alter their original state and reduce their legibility. Digital techniques are widely applied for document preservation, archiving, analysis and dissemination over the Internet. The goal of restoration is to improve the document legibility for both human and automatic operators, to extract patterns and enhance colour reproduction. Our paper presents a framework for virtual restoration of ancient documents based on a combination of multispectral acquisition, 3D imaging and digital image analysis. The proposed framework consists of several steps. First, digital representations of the documents are acquired as multispectral images and 3D surface maps, the latter reconstructed by a structured light technique. A multispectral camera and a digital projector are used in triangular configuration for 2D and 3D data acquisition. Then the multispectral images are registered against possible misalignments, and the 3D surface representation is used to correct geometrical distortions. Document flattening is then performed by 3D surface parameterization and texture mapping. Statistical techniques of decorrelation are applied to extract individual context parts of the document patterns (stamp, text, etc.) and to attenuate interferences. The processed data are then binarized by the proper segmentation technique. The entire digital object history all the acquisition and processing steps, with the corresponding parameters is stored in metadata files. These data can be exploited in a future evaluation of the restoration process and can be used for either the creation of an expert knowledge database or the extraction of cross-document observations and conclusions.

Deep learning-based methods have made significant impact and demonstrated superior performance for the classical image and video super-resolution (SR) tasks. Yet, deep learning-based approaches to super-resolve the appearance of 3D objects are still sparse. Due to the nature of rendering 3D models, 2D SR methods applied directly to 3D object texture may not be a good approach. In this paper, we propose a rendering loss derived from the rendering of a 3D model and demonstrate its application to the SR task in the context of 3D texturing. Unlike other literature on the 3D appearance SR, no geometry information of the 3D model is required during network inference. Experimental results demonstrate that incorporating the rendering loss during network training outperforms existing state-of-the-art methods for 3D appearance SR. Furthermore, we provide a new 3D dataset consisting of 97 complete 3D models for further research in this field.

A class of techniques in computer vision and graphics is based on capturing multiple images of a scene under different illumination conditions. These techniques explore variations in illumination from image to image to extract interesting information about the scene. However, their applicability to dynamic environments is limited due to the need for robust motion compensation algorithms. To overcome this issue, we propose a method to separate multiple illuminants from a single image. Given an image of a scene simultaneously illuminated by multiple light sources, our method generates individual images as if they had been illuminated by each of the light sources separately. To facilitate the illumination separation process, we encode each light source with a distinct sinusoidal pattern, strategically selected given the relative position of each light with respect to the camera, such that the observed sinusoids become independent of the scene geometry. The individual illuminants are then demultiplexed by analyzing local frequencies. We show applications of our approach in image-based relighting, photometric stereo, and multiflash imaging.

Numerical analysis of cartilage surfaces for osteoarthritis diagnosis using field and feature parameters

In this paper, we proposed improved single image super resolution using multiple re-evaluation Process model for use in various image processing fields. The proposed method generates the first super resolution using the input image, and analyzes the change for each region by comparing the features of previous image and super resolution result. According to the analyzed features, the feature map for generate n-th super resolution was selected for improved detail. After then, next generate super resolution using previous super resolution result as input image. This process is repeated for final result. The existing single image super resolution method has two areas to be improved. First, it minimizes artifacts or staircases, which are unnecessary details that can be created during the super resolution process. Second, it is necessary to consider the input image because it affects the result depending on the quality of input image used in the super resolution process. Therefore, in order to minimize unnecessary details, the proposed method analyzed the feature map from the generated super resolution result and applied it according to the amount of change. In addition, aimed to gradually improve the input image to be used in the super resolution process by using the super resolution generated in the previous step. By comparing and evaluating the proposed method with the conventional single image super resolution method with PSNR and SSIM, it is improved by about 3%.

Compared with geometric stereo vision based on triangulation principle, photometric stereo method has advantages in recovering per-pixel surface details. In this paper, we present a practical 3D imaging system by combining the near-light photometric stereo and the speckle-based stereo matching method. The system is compact in structure and suitable for multi-albedo targets. The parameters (including position and intensity) of the light sources can be self-calibrated. To realize the auto-calibration, we first use the distant lighting model to estimate the initial surface albedo map, and then with the estimated albedo map and the normal vector field fixed, the parameters of the near lighting model are optimized. Next, with the optimized lighting model, we use the near-light photometric stereo method to re-compute the surface normal and fuse it with the coarse depth map from stereo vision to achieve high-quality depth map. Experimental results show that our system can realize high-quality reconstruction in general indoor environments.

Semiconductor manufacturers spend hundreds of millions of dollars and years of development time to create a new manufacturing process and to design frontrunner products to work on the new process. A considerable percentage of this large investment is aimed at producing the process design rules and related lithography technology to pattern the new products successfully. Significant additional cost and time is needed in both process and design development if the design rules or lithography strategy must be modified. Therefore, early and accurate prediction of both process design rules and lithography options is necessary for minimizing cost and timing in semiconductor development. This paper describes a methodology to determine the optimum design rules and lithography conditions with high accuracy early in the development lifecycle. We present results from the 32nm logic node but the methodology can be extended to the 22nm node or any other node. This work involves: automated generation of extended realistic logic test layouts utilizing programmed teststructures for a variety of design rules; determining a range of optical illumination and process conditions to test for each critical design layer; using these illumination conditions to create a extrapolatable process window OPC model which is matched to rigorous TCAD lithography focus-exposure full chemically amplified resist models; creating reticle enhancement technique (RET) recipes which are flexible enough to be used over a variety of design rule and illumination conditions; OPC recipes which are flexible enough to be used over a variety of design rule and illumination conditions; and OPC verification to find, categorize and report all patterning issues found in the different design and illumination variations. In this work we describe in detail the individual steps in the methodology, and provide results of its use for 32nm node design rule and process optimization.