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.

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.

DELAUNAY TRIANGULATION AND IMAGE DENSE MATCHING IN VIEW SYNTHESIS

A new method of view synthesis is proposed based on Delaunay triangulation. The first step of this method is making the Delaunay triangulation of 2 reference images. Secondly, matching the image points using the epipolar geometry constraint. Finally, constructing the third view according to pixel transferring under the trilinear constraint. The method gets rid of the classic time consuming dense matching technique and takes advantage of Delaunay triangulation. So it can not only save the computation time but also enhance the quality of the synthesized view. The significance of this method is that it can be used directly in the fields of video coding, image compressing and virtual reality.

Three-Dimensional Space Interpolation of Grey / Depth Image Sequence-A New Technique of Computer Graphics Synthesis

This paper advances a three-dimensional space interpolation method of grey / depth image sequence, which breaks free from the limit of original practical photographing route. Pictures can cruise at will in space. By using space sparse sampling, great memorial capacity can be saved and reproduced scenes can be controlled. To solve time consuming and complex computations in three-dimensional interpolation algorithm, we have studied a fast and practical algorithm of scattered space lattice and that of 'Warp' algorithm with proper depth. By several simple aspects of three dimensional space interpolation, we succeed in developing some simple and practical algorithms. Some results of simulated experiments with computers have shown that the new method is absolutely feasible.

A Survey of GAN Based Image Synthesis

Image generation is a hot topic in the academic recently,and has been applied to AI drawing,which can bring Vivid AI paintings without labor costs.In image generation,we represent the image as a random vector,assuming that the images of the natural scene obey an unknown distribution,we hope to estimate its distribution through some observation samples.Especially,with the development of GAN(Generative Adversarial Network),The generator and discriminator improve the model capability through adversarial,the quality of the generated image is also increasing.The image quality generated by the existing GAN based image generation model is so well-paint that it can be passed for genuine one.Based on the brief introduction of the concept ofGAN,this paper analyzes themain ideas of image synthesis,studies the representative SOTA GAN based Image synthesis method.

View synthesis based on the serial images from camera lengthways motion

For the pre-acquired serial images from camera lengthways motion, a view synthesis algorithm based on epipolar geometry constraint is proposed in this paper. It uses the whole matching and maintaining order characters of the epipolar line, Fourier transform and dynamic programming matching theories, thus truly synthesizing the destination image of current viewpoint. Through the combination of Fourier transform, epipolar geometry constraint and dynamic programming matching, the circumference distortion problem resulting from conventional view synthesis approaches is effectively avoided. The detailed implementation steps of this algorithm are given, and some running instances are presented to illustrate the results.

Recent progress in the green synthesis of rare-earth doped upconversion nanophosphors for optical bioimaging from cells to animals

Rare-earth doped upconversion nanophosphors(UCNPs), which convert low energy near-infrared(NIR) photons into high energy photons such as ultraviolet, visible light and NIR light, have found various applications in optical bioimaging. In this review article, we summarize recent advances in the synthesis and applications of UCNPs achieved by us and other groups in the past few years. The approaches for the synthesis of UCNPs are presented,with an emphasis on the role of green chemistry in the advancement of this field, followed by a focused overview on their latest applications in optical bioimaging from subcellular structures through cells to living animals. Challenges and opportunities for the use of UCNPs in biomedical diagnosis and therapy are discussed.

A Novel Unsupervised MRI Synthetic CT Image Generation Framework with Registration Network

In recent years,radiotherapy based only on Magnetic Resonance(MR)images has become a hot spot for radiotherapy planning research in the current medical field.However,functional computed tomography(CT)is still needed for dose calculation in the clinic.Recent deep-learning approaches to synthesized CT images from MR images have raised much research interest,making radiotherapy based only on MR images possible.In this paper,we proposed a novel unsupervised image synthesis framework with registration networks.This paper aims to enforce the constraints between the reconstructed image and the input image by registering the reconstructed image with the input image and registering the cycle-consistent image with the input image.Furthermore,this paper added ConvNeXt blocks to the network and used large kernel convolutional layers to improve the network’s ability to extract features.This research used the collected head and neck data of 180 patients with nasopharyngeal carcinoma to experiment and evaluate the training model with four evaluation metrics.At the same time,this research made a quantitative comparison of several commonly used model frameworks.We evaluate the model performance in four evaluation metrics which achieve Mean Absolute Error(MAE),Root Mean Square Error(RMSE),Peak Signal-to-Noise Ratio(PSNR),and Structural Similarity(SSIM)are 18.55±1.44,86.91±4.31,33.45±0.74 and 0.960±0.005,respectively.Compared with other methods,MAE decreased by 2.17,RMSE decreased by 7.82,PSNR increased by 0.76,and SSIM increased by 0.011.The results show that the model proposed in this paper outperforms other methods in the quality of image synthesis.The work in this paper is of guiding significance to the study of MR-only radiotherapy planning.

Tight Sandstone Image Augmentation for Image Identification Using Deep Learning

Intelligent identification of sandstone slice images using deep learning technology is the development trend of mineral identification,and accurate mineral particle segmentation is the most critical step for intelligent identification.A typical identification model requires many training samples to learn as many distinguishable features as possible.However,limited by the difficulty of data acquisition,the high cost of labeling,and privacy protection,this has led to a sparse sample number and cannot meet the training requirements of deep learning image identification models.In order to increase the number of samples and improve the training effect of deep learning models,this paper proposes a tight sandstone image data augmentation method by combining the advantages of the data deformation method and the data oversampling method in the Putaohua reservoir in the Sanzhao Sag of the Songliao Basin as the target area.First,the Style Generative Adversarial Network(StyleGAN)is improved to generate high-resolution tight sandstone images to improve data diversity.Second,we improve the Automatic Data Augmentation(AutoAugment)algorithm to search for the optimal augmentation strategy to expand the data scale.Finally,we design comparison experiments to demonstrate that this method has obvious advantages in generating image quality and improving the identification effect of deep learning models in real application scenarios.

An Approach to Synthesize Diverse Underwater Image Dataset

Images that are taken underwater mostly present color shift with hazy effects due to the special property of water.Underwater image enhancement methods are proposed to handle this issue.However,their enhancement results are only evaluated on a small number of underwater images.The lack of a sufficiently large and diverse dataset for efficient evaluation of underwater image enhancement methods provokes the present paper.The present paper proposes an organized method to synthesize diverse underwater images,which can function as a benchmark dataset.The present synthesis is based on the underwater image formation model,which describes the physical degradation process.The indoor RGB-D image dataset is used as the seed for underwater style image generation.The ambient light is simulated based on the statistical mean value of real-world underwater images.Attenuation coefficients for diverse water types are carefully selected.Finally,in total 14490 underwater images of 10 water types are synthesized.Based on the synthesized database,state-of-the-art image enhancement methods are appropriately evaluated.Besides,the large diverse underwater image database is beneficial in the development of learning-based methods.

Cross-Modal Consistency with Aesthetic Similarity for Multimodal False Information Detection

With the explosive growth of false information on social media platforms, the automatic detection of multimodalfalse information has received increasing attention. Recent research has significantly contributed to multimodalinformation exchange and fusion, with many methods attempting to integrate unimodal features to generatemultimodal news representations. However, they still need to fully explore the hierarchical and complex semanticcorrelations between different modal contents, severely limiting their performance detecting multimodal falseinformation. This work proposes a two-stage detection framework for multimodal false information detection,called ASMFD, which is based on image aesthetic similarity to segment and explores the consistency andinconsistency features of images and texts. Specifically, we first use the Contrastive Language-Image Pre-training(CLIP) model to learn the relationship between text and images through label awareness and train an imageaesthetic attribute scorer using an aesthetic attribute dataset. Then, we calculate the aesthetic similarity betweenthe image and related images and use this similarity as a threshold to divide the multimodal correlation matrixinto consistency and inconsistencymatrices. Finally, the fusionmodule is designed to identify essential features fordetectingmultimodal false information. In extensive experiments on four datasets, the performance of the ASMFDis superior to state-of-the-art baseline methods.

Synthesis, radiolabeling and animal studies of [^(131)I]MPPI: A 5-HTB_(1A) imaging agent

The synthesis and biological evaluation of serotonin (5-HTB1AB) imaging agent [P131PI]- 4-iodo-N-{2-[4-(2-methoxyphenyl)-piperazin-1-yl]-ethyl}-N-pridin-2-yl-benzamide ([P131PI]MPPI ) are reported. The chemical structure of aimed compound and intermediates were confirmed by IR, P1PHNMR, and MS. Radiochemical purity was above 99% determined by TLC. Biodistribution of [P131PI]MPPI in rats displayed high uptake in hippocam-pus and low uptake in cerebellum. The ratio of the uptake of [P131PI]MPPI in hippocampus to that in cerebellum was 2.90 at 30 min post injection. The radioactivity in thyroid was 0.069 and 0.128% ID/g organ at 5 min and 120 min, respectively, and it was increased with time, which suggests that in vivo deiodination may be the major route of me-tabolism. Ex vivo autoradiography of brain section displayed significant decrease of radioactivity in hippocampus when pretreated with 8-OH-DPAT, a selective 5HTB1AB agonist, compared with control. These findings strongly sug-gested that P131PI-MPPI could be used as an in vivo marker for studies of pharmacology of the 5-HTB1AB receptor system in animals.

Semi-automated synthesis,validation and microPET imaging of ^(18)F-FP-DTBZ as a vesicular monoamine transporter ligand

This work was to develop a semi-automated synthesis of 18F-9-fluoropropyl-9-desmethyl-DTBZ (18F-FP-DTBZ) and validate its potential as a vesicular monoamine transporter 2 (VMAT2) ligand.18F-FP-DTBZ was synthesized by a semi-automated procedure in a 21-35% yield without decay correction and with a radiochemical purity of >98%.Bioistribution in rats exhibited a favorable brain uptakes of the ligand (0.31±0.04 ID% at 60min post injection,n=8).The highest radioactivity located in VMAT2 enriched striatal tissue.The target-to-nontarget ratio (striatum/cerebellum,ST/CB) was 4.81±0.84.Blocking studies implied that striatum uptake could be blocked by DTBZ (a VMAT2 inhibitor) but could not by CFT (a dopamine transporter inhibitor).MicroPET imaging with 18F-FP-DTBZ in normal rats gave high quality images in which high radioactivity were observed in the striatal tissue.Time-and-activity curves revealed good retention in the target (striatum) and rapid clearance in the background (cerebellum),which resulted in a maximum ST/CB ratio of 5.08±0.81 (n=3) in 80-120min.By contrast,the 6-hydroxydopamine unilateral lesioned rats gave asymmetrical striata images with higher 18F-FP-DTBZ concentration on the unlesioned side (unlesioned-ST/CB=5.21±0.38,n=3) than the lesioned (lesioned-ST/CB=2.34±0.51).The results validated that 18F-FP-DTBZ is a favorable PET ligand binding to VMAT2.

基于Image Quilting算法的纹理合成

基于样图的纹理合成方法是继纹理映射、过程纹理合成方法后发展起来的纹理拼接技术,用于解决传统方法中出现的缝隙、扭曲、变形和参数调整等问题。Image Quilting算法是简单易行的接缝消除方法,采用Image Quilting算法合成纹理并生成Wang Tile集,继而拼接大图像。实验表明,Image Quilting算法能够较好地保持纹理特征的连续性,但在接缝处可能破坏纹元特征。

Image Quilting纹理合成算法的实现与改进

针对Image Quilting算法在合成结构性较强的纹理图片时产生局部纹理不连续的问题,提出了一种改进的方法。改进从以下两方面进行:通过图像变形增加样本图的采样空间以增大匹配的概率;改变匹配块的选择策略以提高合成质量。实验结果表明,改进后的算法有效地改善了纹理不连续现象,生成的纹理图像质量更高。

多核并行技术在Image Quilting纹理合成算法中的应用

针对Image Quilting纹理合成算法的新旧纹理块拼接特点,研究了一种创建多任务实现纹理块切割拼接的并行编程模式.设计了重叠区域缝合路径计算的多任务模块函数iq(),利用MATLAB多核集群中创建的调度器将子任务分配到各个节点上并行执行计算.实验结果表明,该算法获得了较好的加速比,提高了多核CPU的使用效率,有效地提升了多核计算机资源的利用率.

Image quilting纹理传输算法的改进研究

纹理合成与纹理传输是目前计算机图形图像领域比较热门的研究方向.目前针对纹理传输的研究大部分都集中在纹理块与目标图像对应像素颜色信息的匹配度的改进上,目标图像整体的结构信息鲜有提及.以Efros等人提出的image quilting纹理合成算法为基础,提出一种新的纹理传输改进算法,新算法主要对纹理传输过程中的误差匹配公式作了改进,引入了与目标图像的边缘梯度信息有关的项,同时还采用了亮度重映射方法对纹理块进行匹配预处理以解决因纹理图像和目标图像亮度差过大而带来的纹理块搜索困难问题.广泛的实验表明改进后的纹理传输算法在时间上和image quilting算法相差无几,但传输后的图像整体轮廓清晰、亮度明显,因此传输效果更合理、更加符合人的视觉感受.

Embedding Image Through Generated Intermediate Medium Using Deep Convolutional Generative Adversarial Network

Deep neural network has proven to be very effective in computer vision fields.Deep convolutional network can learn the most suitable features of certain images without specific measure functions and outperform lots of traditional image processing methods.Generative adversarial network(GAN)is becoming one of the highlights among these deep neural networks.GAN is capable of generating realistic images which are imperceptible to the human vision system so that the generated images can be directly used as intermediate medium for many tasks.One promising application of using GAN generated images would be image concealing which requires the embedded image looks like not being tampered to human vision system and also undetectable to most analyzers.Texture synthesizing has drawn lots of attention in computer vision field and is used for image concealing in steganography and watermark.The traditional methods which use synthesized textures for information hiding mainly select features and mathematic functions by human metrics and usually have a low embedding rate.This paper takes advantage of the generative network and proposes an approach for synthesizing complex texture-like image of arbitrary size using a modified deep convolutional generative adversarial network(DCGAN),and then demonstrates the feasibility of embedding another image inside the generated texture while the difference between the two images is nearly invisible to the human eyes.

基于Catapult C Synthesis的图像校正算法设计

为解决遥感相机在运动过程中的抖动造成的图像位置偏移问题,提出了一种实时图像校正算法。由于在FPGA中采用HDL进行算法设计难度大、开发周期长,故设计中采用了C语言进行算法设计,然后借助Calypto公司的Catapult C Synthesis工具将抽象的C设计转换成硬件RTL代码。在Catapult C Synthesis中对设计的算法进行了C/C++、RLT协同仿真测试,并在Xilinx XC5VLX110T型FPGA上进行了验证。仿真测试及硬件验证结果表明,采用Catapult C Synthesis设计的算法在时序、性能方面均满足设计要求,能够对偏移的图像进行实时校正。

Layered image inpainting based on image decomposition

We propose a layered image inpainting scheme based on image decomposition. The damaged image is first decomposed into three layers： cartoon, edge, and texture. The cartoon and edge layers are repaired using an adaptive offset operator that can fill-in damaged image blocks while preserving sharpness of edges. The missing information in the texture layer is generated with a texture synthesis method. By using discrete cosine transform （DCT） in image decomposition and trading between resolution and computation complexity in texture synthesis, the processing time is kept at a reasonable level.