Steganography Using Reversible Texture Synthesis Based on Seeded Region Growing and LSB

Steganography technology has been widely used in data transmission with secret information.However,the existing steganography has the disadvantages of low hidden information capacity,poor visual effect of cover images,and is hard to guarantee security.To solve these problems,steganography using reversible texture synthesis based on seeded region growing and LSB is proposed.Secret information is embedded in the process of synthesizing texture image from the existing natural texture.Firstly,we refine the visual effect.Abnormality of synthetic texture cannot be fully prevented if no approach of controlling visual effect is applied in the process of generating synthetic texture.We use seeded region growing algorithm to ensure texture’s similar local appearance.Secondly,the size and capacity of image can be decreased by introducing the information segmentation,because the capacity of the secret information is proportional to the size of the synthetic texture.Thirdly,enhanced security is also a contribution in this research,because our method does not need to transmit parameters for secret information extraction.LSB is used to embed these parameters in the synthetic texture.

Image completion algorithm based on texture synthesis

A new algorithm is proposed for completing the missing parts caused by the removal of foreground or background elements from an image of natural scenery in a visually plausible way. The major contributions of the proposed algorithm are： （1） for most natural images, there is a strong orientation of texture or color distribution. So a method is introduced to compute the main direction of the texture and complete the image by limiting the search to one direction to carry out image completion quite fast; （2） there exists a synthesis ordering for image completion. The searching order of the patches is defined to ensure the regions with more known information and the structures should be completed before filling in other regions; （3） to improve the visual effect of texture synthesis, an adaptive scheme is presented to determine the size of the template window for capturing the features of various scales. A number of examples are given to demonstrate the effectiveness of the proposed algorithm.

Constrained circular-patch-based texture synthesis

Texture synthesis has been developed for several years.The traditional technique can generate a larger image from a small image while avoid feeling of repetition or uncontinuity.Some constrained synthesis methods which can synthesize image according to special location demand or other demands have been also proposed in recent years.However,in general,these constrained texture synthesis methods are simple and have few controllable factors to meet user＇s diverse needs.To control multiple-sample texture synthesis more flexibly in various aspects such as synthesis location,proportion and semantic objects,we present an interactive texture synthesis approach based on circular patches and constrained by objects according to a certain ratio.With this approach,source exemplars and the target image are firstly divided into several regions with different characters.Users can click the blocks in the source exemplars and the want-to-be-synthesized region in the target image,and then texture in the target image is synthesized with the corresponding regions in the source exemplars.In the process of texture synthesis,circular patch instead of square patch is used to eliminate the aliasing phenomena.Images are synthesized from multiple sample images with ratio constraint and experiments on images show that our approach can get effective results of ratio-constrained multi-sample synthesis.

Using texture synthesis in fractal pattern design

Traditional fractal pattern design has some disadvantages such as inability to effectively reflect the characteristics of real scenery and texture. We propose a novel pattern design technique combining fractal geometry and image texture synthesis to solve these problems. We have improved Wei and Levoy (2000)’s texture synthesis algorithm by first using two-dimensional autocorrelation function to analyze the structure and distribution of textures, and then determining the size of L neighborhood. Several special fractal sets were adopted and HSL (Hue, Saturation, and Light) color space was chosen. The fractal structure was used to manipulate the texture synthesis in HSL color space where the pattern’s color can be adjusted conveniently. Experiments showed that patterns with different styles and different color characteristics can be more efficiently generated using the new technique.

STATE:Learning structure and texture representations for novel view synthesis

Novel viewpoint image synthesis is very challenging,especially from sparse views,due to large changes in viewpoint and occlusion.Existing image-based methods fail to generate reasonable results for invisible regions,while geometry-based methods have difficulties in synthesizing detailed textures.In this paper,we propose STATE,an end-to-end deep neural network,for sparse view synthesis by learning structure and texture representations.Structure is encoded as a hybrid feature field to predict reasonable structures for invisible regions while maintaining original structures for visible regions,and texture is encoded as a deformed feature map to preserve detailed textures.We propose a hierarchical fusion scheme with intra-branch and inter-branch aggregation,in which spatio-view attention allows multi-view fusion at the feature level to adaptively select important information by regressing pixel-wise or voxel-wise confidence maps.By decoding the aggregated features,STATE is able to generate realistic images with reasonable structures and detailed textures.Experimental results demonstrate that our method achieves qualitatively and quantitatively better results than state-of-the-art methods.Our method also enables texture and structure editing applications benefiting from implicit disentanglement of structure and texture.Our code is available at http://cic.tju.edu.cn/faculty/likun/projects/STATE.

Digital image inpainting by example-based image synthesis method

A simple and effective image inpainting method is proposed in this paper, which is proved to be suitable for different kinds of target regions with shapes from little scraps to large unseemly objects in a wide range of images. It is an important improvement upon the traditional image inpainting techniques. By introducing a new bijeetive-mapping term into the matching cost function, the artificial repetition problem in the final inpainting image is practically solved. In addition, by adopting an inpainting error map, not only the target pixels are refined gradually during the inpainting process but also the overlapped target patches are combined more seamlessly than previous method. Finally, the inpainting time is dramatically decreased by using a new acceleration method in the matching process.

EFFECT OF COLD ROLLING RATIO OF CATALYST ON SYNTHESIS OF DIAMOND

ＥＦＦＥＣＴＯＦＣＯＬＤＲＯＬＬＩＮＧＲＡＴＩＯＯＦＣＡＴＡＬＹＳＴＯＮＳＹＮＴＨＥＳＩＳＯＦＤＩＡＭＯＮＤ①ＷａｎｇＺｈｉｆａ，ＬｉＷｅｎｘｉａｎ，ＣｈｅｎＪｉａｙａｎＤｅｐａｒｔｍｅｎｔｏｆＭａｔｅｒｉａｌｓＳｃｉｅｎｃｅａｎｄＥｎｇｉｎｅｒｉｎ...

SYNTHESIS AND CHARACTERIZATION OF LIQUID CRYSTALLINE MULTI-BLOCK COPOLYMERS,POLY[1,6-BIS(4-OXYBENZOYL-OXY)HEXANE TEREPHTHALATE]-b-BISPHENOL A POLYCARBONATE

A series of liquid crystalline multi-block copolymers poly[1.6-bis(4-oxybenzoyl-oxy)hexane terephthalate]-b-bisphenol A polycarbonate (PHTH-6-b-PC) with different segment lengths were synthesized in tetrachloroethane by solution polycondensation in which hydroxyl terminated PC and acyl chloride terminated PHTH-6 were used. It is found that block copolymers with high molecular weight and well-defined structures were obtained. All the block copolymers exhibit a nematic liquid crystalline texture.

Development of Texture Mapping Approaches for Additively Manufacturable Surfaces

manufacturing(AM)technologies have been recognized for their capability to build complex components and hence have ofered more freedom to designers for a long time.The ability to directly use a computer-aided design(CAD)model has allowed for fabricating and realizing complicated components,monolithic design,reducing the number of components in an assembly,decreasing time to market,and adding performance or comfort-enhancing functionalities.One of the features that can be introduced for boosting a component functionality using AM is the inclusion of surface texture on a given component.This inclusion is usually a difcult task as creating a CAD model resolving fne details of a given texture is difcult even using commercial software packages.This paper develops a methodology to include texture directly on the CAD model of a target surface using a patch-based sampling texture synthesis algorithm,which can be manufactured using AM.Input for the texture generation algorithm can be either a physical sample or an image with heightmap information.The heightmap information from a physical sample can be obtained by 3D scanning the sample and using the information from the acquired point cloud.After obtaining the required inputs,the patches are sampled for texture generation according to non-parametric estimation of the local conditional Markov random feld(MRF)density function,which helps avoid mismatched features across the patch boundaries.While generating the texture,a design constraint to ensure AM producibility is considered,which is essential when manufacturing a component using,e.g.,Fused Deposition Melting(FDM)or Laser Powder Bed Fusion(LPBF).The generated texture is then mapped onto the surface using the developed distance and angle preserving mapping algorithms.The implemented algorithms can be used to map the generated texture onto a mathematically defned surface.This paper maps the textures onto fat,curved,and sinusoidal surfaces for illustration.After the texture mapping,a stereolithography(STL)model is generated with the desired texture on the target surface.The generated STL model is printed using FDM technology as a fnal step.

Textile Texture Modeling Using Random Field Models

Described in the paper is the method to fit autoregressive(AR)random field models and Gausslan Markov ran-dom field(GMRF)models for textile textures.Maxi-mum likelihood(ML)estimation schemes and texturesynthesis for these models are presented.Three types oftypical textile textures are tested using the method andsatisfactory results are achieved.

Constructive Texture Steganography Based on Compression Mapping of Secret Messages

This paper proposes a new constructive texture synthesis steganographic scheme by compressing original secret messages.First,we divide the original message into multiple bit blocks,which are transferred to decimal values and compressed into small decimal values by recording their interval sign characters.Then,a candidate pattern is generated by combining the given source pattern and boundary extension algorithm.Furthermore,we segment the candidate pattern into multiple candidate patches and use affine transformation algorithm to locate secret positions on a blank canvas,which are used to hide the sign characters by mapping the candidate patches.Finally,we select the candidate patches with minimal mean square error to represent secret bits to generate stego image by image quilting.Extensive experiments demonstrate that compared with existing texture steganographic methods,our method has a better visual quality,higher embedding capacity and security performance,while maintaining strong anti-steganalysis capability.

Multi-Layer Synthesis of HighlyStructured Texture

In this paper, a novel concept of multilayer synthesis and a general framework for texture synthesis method are presented. Within this framework, we first decompose the texture into the supposed pattern layer and material layer in the frequency domain by an E-texton extracting algorithm, then manipulate and extend them respectively according to their own personalities, and finally merge the newly synthesized pattern layer and material layer again to generate the final output. Experiment results show that our method not only greatly improves the synthesis quality for those cases that single-layer synthesis cannot handle well but also provides an ability of achieving various special synthesis effects.

Method of Direct Texture Synthesis on Arbitrary Surfaces

A direct texture synthesis method on arbitrary surfaces is proposed in thispaper. The idea is to recursively map triangles on surface to texture space until the surface iscompletely mapped. First, the surface is simplified and a tangential vector field is created overthe simplified mesh. Then, mapping process searches for the most optimal texture coordinates intexture sample for each triangle, and the textures of neighboring triangles are blended on the mesh.All synthesized texture triangles are compressed to an atlas. Finally, the simplified mesh issubdivided to approach the initial surface. The algorithm has several advantages over formermethods: it synthesizes texture on surface without local parameterization; it does not needpartitioning surface to patches; and it does not need a particular texture sample. The resultsdemonstrate that the new algorithm is applicable to a wide variety of texture samples and anytriangulated surfaces.

Real-Time Texture Synthesis Using s-Tile Set

This paper presents a novel method of generating a set of texture tiles from samples, which can be seamlessly tiled into arbitrary size textures in real-time. Compared to existing methods, our approach is simpler and more advantageous in eliminating visual seams that may exist in each tile of the existing methods, especially when the samples have elaborate features or distinct colors. Texture tiles generated by our approach can be regarded as single-colored tiles on each orthogonal direction border, which are easier for tiling and more suitable for sentence tiling. Experimental results demonstrate the feasibility and effectiveness of our approach.

Orientation Field Guided Texture Synthesis

We present a framework for example-based texture synthesis with feature directions aligned to vector fields with 2-way rotational symmetry, also known as orientation fields. Through a simple variational formulation, the framework allows the user to design the orientation field with intuitive controls, by interactively manipulating singularities and field directions. The resulting field is then used to guide a parallel synthesis. Our contribution is twofold： a design tool for orientation fields with a natural boundary condition, and a parallel texture synthesis adapted specifically for using such fields in feature alignment. We demonstrate the advantages of the procedure through examples on planar and curved patches with trivial topology.

Co-occurrence based texture synthesis

As image generation techniques mature,there is a growing interest in explainable representations that are easy to understand and intuitive to manipulate.In this work,we turn to co-occurrence statistics,which have long been used for texture analysis,to learn a controllable texture synthesis model.We propose a fully convolutional generative adversarial network,conditioned locally on co-occurrence statistics,to generate arbitrarily large images while having local,interpretable control over texture appearance.To encourage fidelity to the input condition,we introduce a novel differentiable co-occurrence loss that is integrated seamlessly into our framework in an end-to-end fashion.We demonstrate that our solution offers a stable,intuitive,and interpretable latent representation for texture synthesis,which can be used to generate smooth texture morphs between different textures.We further show an interactive texture tool that allows a user to adjust local characteristics of the synthesized texture by directly using the co-occurrence values.

Texture synthesis via the matching compatibility between patches

A new patch-based texture synthesis method is presented in this paper. By the method, a set of patches that can be matched with a sampled patch for growing textures effectively, called the matching compatibility between patches, is generated first for each patch, and the set is further optimized by culling the patches that may cause synthesis conflicts. In this way, similarity measurement calculation for selecting suitable patches in texture synthesis can be greatly saved, and synthesis conflicts between neighbouring patches are substantially reduced. Furthermore, retrace computation is integrated in the synthesis process to improve the texture quality. As a result, the new method can produce high quality textures as texture optimization, the best method to date for producing good textures, and run in a time complexity linear to the size of the output texture. Experimental results show that the new method can interactively generate a large texture in 1024 × 1024 pixels, which is very difficult to achieve by existing methods.

Geometry Texture Synthesis Based on Laplacian Texture Image

In this paper, we present a new method to synthesize geometric texture details on an arbitrary surface from a sample texture patch. The key idea is to use Laplacian texture images to represent geometric texture details, which in turn facilitate simple and effective geometry texture synthesis and enable flexible geometry texture editing. Given a sample model and a target model, we first select a patch from the sample model and extract the geometric texture details. Next, we construct a Laplacian texture image for the extracted texture and synthesize the Laplacian texture image to the target model using an image texture synthesis technique on surface. Finally, we reconstruct the textured target model with adjusted Laplacian coordinates. Compared to the existing methods, our method is easy-to-implement and produces results of high quality. Furthermore, it performs flexible control on the appearance of the textured target model through operations on the Laplacian texture image.

Seamless and non-repetitive 4D texture variation synthesis and real-time rendering for measured optical material behavior

We show how to overcome the single weakness of an existing fully automatic system for acquisition of spatially varying optical material behavior of real object surfaces. While the expression of spatially varying material behavior with spherical dependence on incoming light as a 4D texture(an ABTF material model) allows flexible mapping onto arbitrary 3D geometry, with photo-realistic rendering and interaction in real time,this very method of texture-like representation exposes it to common problems of texturing, striking in two disadvantages. Firstly, non-seamless textures create visible artifacts at boundaries. Secondly, even a perfectly seamless texture causes repetition artifacts due to their organised placement in large numbers over a 3D surface. We have solved both problems through our novel texture synthesis method that generates a set of seamless texture variations randomly distributed over the surface at shading time. When compared to regular 2D textures, the inter-dimensional coherence of the 4D ABTF material model poses entirely new challenges to texture synthesis, which includes maintaining the consistency of material behavior throughout the 4D space spanned by the spatial image domain and the angular illumination hemisphere. In addition, we tackle the increased memory consumption caused by the numerous variations through a fitting scheme specifically designed to reconstruct the most prominent effects captured in the material model.

Interactive texture design and synthesis from mesh sketches

Geometry mesh introduces user control into tex- ture synthesis and editing, and brings more variations in the synthesized results. But still two problems related remain in need of better solutions. One problem is generating the meshes with desired size and pattern efficiently from easier user inputs. The other problem is improving the quality of synthesized results with mesh information. We present a new two-step texture design and synthesis method that addresses these two problems. Besides example texture, a small piece of mesh sketch drawn by hand or detected from example texture is input to our algorithm. And then a mesh synthesis method of geometry space is provided to avoid optimizations cell by cell. Distance and orientation features are introduced to im- prove the quality of mesh rasterization. Results show that with our method, users can design and synthesize textures from mesh sketches easily and interactively.