Source Our Result Gatys Et Al We introduce a novel multi scale approach for synthesizing high resolution natural textures using convolutional neural networks trained on image classification tasks. In this paper, we develop a multiscale texture synthesis algorithm. we propose a novel example based representation, which we call an exemplar graph, that simply requires a few low resolution input exemplars at different scales.

Source Our Result Gatys Et Al We show the benefits of our analysis in several texture synthesis applications including content selection, non stationary synthesis, interactive painting at multiple scales, and choice of tile size. The seminal paper on neural texture synthesis is texture synthesis using convolutional neural networks, published in 2015 by gatys, et al, i re implemented it here for those interested. In this work, we present several neural synthesis meth ods that significantly improve the ability to preserve the large scale organization of textures. we first propose a simple multi resolution framework that account for large scale structures and permits the synthesis of high resolution images. In this repository we implement a tileable texture synthesis algorithm, which can theoretically synthesize high resolution textures with an infinite size.

Source Our Result Gatys Et Al In this work, we present several neural synthesis meth ods that significantly improve the ability to preserve the large scale organization of textures. we first propose a simple multi resolution framework that account for large scale structures and permits the synthesis of high resolution images. In this repository we implement a tileable texture synthesis algorithm, which can theoretically synthesize high resolution textures with an infinite size. In the traditional texture synthesis world this issue had already been addressed with the concept of a [gaussian pyramid] (rca eng. 1984 adelson.pdf) multi scale image representation. it turns out that combining these two ideas is both simple and powerful for building high resolution images. The algorithm designs input exemplar textures using graph structure, constructs an exemplar relation graph, adopts patch matching synthesis, introduces optimization to keep patch matching properly, and the size of the block can be adjusted and a jitter function was proposed. Like other algorithms we utilise an implicit texture model by copying arbitrary shaped texture patches from the sample to the destination over a multi scale image pyramid. our method combines the advantages of different previous techniques with respect to quality. To address these challenges, we propose an innovative multi scale texture fusion for reference based super resolution via a state space model, which enables efficient multi scale feature fusion and long term dependency modeling for better texture restoration.