Description: 图像修复的经典文献：Image inpainting by global structure and texture propagation-Image Inpainting classic literature: Image inpainting by global structure and texture propagation Platform: |
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Author:chenlx |
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Description: 基于C实现的采样复制的纹理图像修复方法。可去除遮罩物 -C based on the realization of the sample copy of the texture image restoration method. Mask material can be removed Platform: |
Size: 1783808 |
Author:犇 |
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Description: This program has two parts, first of all the original picture is devided into regions based on the texture, then the inpainting process begins with the segmented picture as a reference. Platform: |
Size: 504832 |
Author:xinhui |
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Description: 基于纹理的形状恢复和纹理的应用,利用离散小波框架模极值共生距阵的分类算法，能够很好的修得复大块缺损的图像-Based on the shape and texture, using the texture application framework of discrete wavelet modulus maximum symbiosis of classification algorithm is very good, can you answer large defect image Platform: |
Size: 1796096 |
Author:张开 |
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Description: Image inpainting algorithm combining both attempts: texture synthesis and image inpainting. Works especially well for texture and structure propagation, and filling-in large regions. Platform: |
Size: 994304 |
Author:elwismw |
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Description: 图像修复是图像处理的一个重要问题。这是一个困难的问题，同时填写在地区缺少图像信息的纹理和结构。以损坏的图像inpaint失踪的结构和纹理信息，基于小波分解的图像修补算法提出。首先损坏的图像分解成子图像和纹理结构，利用小波子图像转变。然后，地区结构中缺少的信息的子图像重建曲率驱动扩散（CDD），算法，而在同一区域的纹理子图像填写基于改进的纹理合成的典范 最后，恢复的图像，通过重组，结构和纹理恢复的结果。大量的实验表明，该算法可以快速，高效地恢复在同一时间的结构和纹理信息，视觉效果和峰值信噪比比（PSNR）比同类算法更好。-Image inpainting is an important problem image processing. It is a difficult problem to simultaneously fill-in the
texture and structure in regions of missing image information. In order to inpaint the damaged image with both
missing the structure and texture information, an image inpainting algorithm based on wavelet decomposition is
presented. First the damaged image is decomposed into structure sub-image and texture sub-image using the wavelet
transformation. Then, the sub-image with the region of missing information in the structure is reconstructed by
Curvature-Driven Diffusions (CDD) algorithm, while the same region in the texture sub-image is filled-in with the
improved texture synthesis based on exemplar Finally, the restored image is given by recombining the structure and
texture restored results. A large number of experiments show that the proposed algorithm can quickly and efficiently
restore the structure and texture information at the same time, and the visual eff Platform: |
Size: 245760 |
Author:Ankiss |
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Description: ：刚像修复足指重建损坏的I訇像业女除粥像巾币需要的对蒙的过程。我们需
要采取蛙适当的方，℃修补县原始状忐帕l锢形，同时确保修补结果达到最佳的艺术
效果或者说视觉效粜。日前有一嗤比较流行的修复打法．如基于偏微分力程的修
复理沦或壁于纹理台成的修复理f}色．它们分别在细节修复和规则纹理修复案例中
仃良好的表现。假在受损区域过大或完全丢失了个语义片段的情况卜，邢些传
统方法常常难以胜任。柱J比较从待修复l刘像自身款取信息进行修补，使用从其他
图像内弁叶】引入新的信息柬修复艘损图像的修补方式·叮能更有帮助-Image inpainting rethrs to the process of reconswacting the damaged
image or removal of unwanted objects in the image We need to take the most
appropriate way of inpainfing the image to its original state．while ensuring that the
result achieves the best artistic eff}ect There are some methods commonly used such as
method based Oil the theory of PDE or based 011 texture synthesis These methods
performed well in detail reconstrucrion and regular texture synthesis cases respectively
But in cases that the damaged region is too large or a semmltic fragment is completely
missed．those old methods cannot do a good Job Platform: |
Size: 522240 |
Author:孙红娟 |
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Description: A new algorithm is proposed for removing large objects from
digital images. The challenge is to fill in the hole that is left behind in a
visually plausible way.
In the past, this problem has been addressed by two classes of algorithms:
(i) “texture synthesis” algorithms for generating large image regions
sample textures, and (ii) “inpainting” techniques for filling in
small image gaps. The former has been demonstrated for “textures” – repeating
two-dimensional patterns with some stochasticity the latter focus
on linear “structures” which can be thought of as one-dimensional patterns,
such as lines and object contours.-A new algorithm is proposed for removing large objects from
digital images. The challenge is to fill in the hole that is left behind in a
visually plausible way.
In the past, this problem has been addressed by two classes of algorithms:
(i) “texture synthesis” algorithms for generating large image regions
sample textures, and (ii) “inpainting” techniques for filling in
small image gaps. The former has been demonstrated for “textures” – repeating
two-dimensional patterns with some stochasticity the latter focus
on linear “structures” which can be thought of as one-dimensional patterns,
such as lines and object contours. Platform: |
Size: 1841152 |
Author:stary |
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Search - texture inpainting