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Image Restoration Based on Scene Adaptive Patch In-painting for Tampered Natural Scenes

机译:基于场景自适应补丁绘画的图像恢复,用于篡改自然场景

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Many Researchers proposed algorithms which restored damaged images. These methods cause textures broken while inpainting texture image with complex structure. Most of the existing inpainting techniques require knowing beforehand where those damaged pixels are, either given as a priori or detected by some preprocessing. However, in certain applications, such information is neither available nor can be reliably pre-detected, like noise from archived photographs. This paper propose a patch based adaptive inpainting model to solve these types of problems, i.e., a model of simultaneously identifying and recovering damaged pixels of the given image. The proposed inpainting method is applied to various challenging image restoration tasks, including recovering images that are blurry and damaged by scratches. The experimental result shows that it is effective in inpainting complex texture images.
机译:许多研究人员提出了恢复损坏的图像的算法。这些方法导致纹理破碎,同时采用复杂结构修复纹理图像。大多数现有的染色技术需要事先知道那些损坏的像素是作为先验的或通过一些预处理检测到的那些损坏的像素。然而,在某些应用中,这种信息既不可用,也不能可靠地预先检测,例如来自存档照片的噪声。本文提出了一种基于贴片的自适应修复模型来解决这些类型的问题,即同时识别和恢复给定图像的损坏像素的模型。所提出的初始化方法应用于各种具有挑战性的图像恢复任务,包括恢复图像的图像模糊并被划痕损坏。实验结果表明,它在修复复杂的纹理图像方面是有效的。

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