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Texture synthesis is the process of algorithmically constructing a large digital image from a small digital sample image by taking advantage of its structural content. It is an object of research in computer graphics and is used in many fields, amongst others digital image editing , 3D computer graphics and post-production of films .
NST is an example of image stylization, a problem studied for over two decades within the field of non-photorealistic rendering. The first two example-based style transfer algorithms were image analogies [1] and image quilting. [2] Both of these methods were based on patch-based texture synthesis algorithms.
A neural radiance field (NeRF) is a method based on deep learning for reconstructing a three-dimensional representation of a scene from two-dimensional images. The NeRF model enables downstream applications of novel view synthesis, scene geometry reconstruction, and obtaining the reflectance properties of the scene.
Historically, rendering was called image synthesis [6]: xxi but today this term is likely to mean AI image generation. [7] The term "neural rendering" is sometimes used when a neural network is the primary means of generating an image but some degree of control over the output image is provided. [ 8 ]
Texture transfer, in which images are "texturized" with some arbitrary source texture. Artistic filters, in which various drawing and painting styles, including oil, pastel, and pen-and-ink rendering, are synthesized based on scanned real-world examples.
In this example: A depends on B and D. B depends on A and D. D depends on A, B, and E. E depends on D and C. C depends on E. In the domain of physics and probability , a Markov random field ( MRF ), Markov network or undirected graphical model is a set of random variables having a Markov property described by an undirected graph .
The 3D geometry and textures are captured onto a 3D model by a 3D reconstruction method, such as sampling the target by means of 3D scanning with an RGB XYZ scanner such as Arius3d or Cyberware (textures from photos, not pure RGB XYZ scanner), stereophotogrammetrically from synchronized photos or even from enough repeated non-simultaneous photos.
Sparse dictionary learning has been successfully applied to various image, video and audio processing tasks as well as to texture synthesis [15] and unsupervised clustering. [16] In evaluations with the Bag-of-Words model, [17] [18] sparse coding was found empirically to outperform other coding approaches on the object category recognition tasks.