Abstract

A new approach is presented to restore the image from noisy fence images. When people capture the images at Zoos, parks and gardens the fence affects the authentic appeal of the object behind it. Due to the acquisition channels, the noise will be added to the images. So, removal of the fence and noise in these images is necessary to improve the appearance of the desired objects. Segmentation of the fence from the noisy image is very difficult because these are extended into the entire image region. In this paper, segmentation of the fence is done in both noiseless and Gaussian noise corrupted images. Segmentation of the fence is achieved using a graph cut technique. Morphological operations are applied to improve the fence mask. Removal of the fence is done with a hybrid inpainting technique. From the De-fenced image, noise is removed using Conventional Neural Networks. Qualitative and quantitative results show the effectiveness of the proposed approach.

中文翻译：

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嘈杂无声的栅栏遮挡图像的恢复

摘要

提出了一种从嘈杂的栅栏图像中还原图像的新方法。当人们在动物园，公园和花园中捕获图像时，围栏会影响其后面物体的真实吸引力。由于采集通道的原因，噪声将被添加到图像中。因此，必须去除这些图像中的围栏和噪声以改善所需对象的外观。从嘈杂的图像分割围栏非常困难，因为这些图像会扩展到整个图像区域。在本文中，在无噪声和高斯噪声破坏图像中都对栅栏进行了分割。使用图形切割技术可以实现围栏的分割。进行形态学操作以改善防护罩。围栏的移除是通过混合修补技术完成的。从防御图像中，使用常规神经网络可以消除噪音。定性和定量结果表明了该方法的有效性。