We investigate generative adversarial neural networks (GAN) for remote sensing image inpainting We are considering a generative neural network with a contour predictor We use this neural network to inpainting of the natural remote sensing images obtained by “Canopus”, “Meteor”, “AIST”, “Resurs” aircrafts, as well as Google Earth images As a basis for comparison, we use an exemplar-based algorithm We experimentally prove the effectiveness of the generative neural network with the contour predictor for remote sensing image inpainting, in particular for generation forgery Earth remote sensing data

Remote Sensing Image Inpainting with Generative Adversarial Networks

This paper proposes a novel image inpainting algorithm based on sparse representation with histogram dictionary. In the propose method, it is a patch-based inpainting method, a patch as the basic unit of inpainting algorithm. Instead of using all the known image patches to form a dictionary, we propose to construct a histogram dictionary by using the histogram. If the histogram between an image patch which cuts from the source region of the input image and the target patch are similar, the image patch as the similar patch is selected to generate a similar dictionary for inpainting algorithm. Two kinds of similarity methods are proposed, so two kinds of histogram dictionaries are corresponding. These histogram dictionaries can ensure a large related to the target patch. Experimental results show that the proposed inpainting method using the histogram dictionary can efficiently inpainting of missing areas.

/pdf/image-inpainting-based-on-sparse-representation-with-qww6dd672v.pdf

Image Inpainting Based on Sparse Representation with Histogram Dictionary

The practice of imperceptibly altering images has a long history in artistic production. In the field of image processing, there have been significant advancements in studying image deblurring, denoising, and inpainting, contributing to picture enhancement and restoration. These studies have garnered widespread recognition due to substantial improvements in recent years. One intriguing area of research in picture restoration is the reconstruction of missing regions, which has various real-world applications. This paper focuses on removing extraneous text material from images through an image inpainting task. The K-SVD approach is utilized to acquire sparse representation dictionaries from the given image. Random and DCT dictionaries are constructed for image processing, and through the application of the K-SVD technique, the dictionaries are trained to adapt effectively to image patches with different properties. 

Image Inpainting Based On Sparse Representation with Dictionary Learning and Construction

The practice of imperceptibly altering images has a long history in artistic production. In the field of image processing, there have been significant advancements in studying image deblurring, denoising, and inpainting, contributing to picture enhancement and restoration. These studies have garnered widespread recognition due to substantial improvements in recent years. One intriguing area of research in picture restoration is the reconstruction of missing regions, which has various real-world applications. This paper focuses on removing extraneous text material from images through an image inpainting task. The K-SVD approach is utilized to acquire sparse representation dictionaries from the given image. Random and DCT dictionaries are constructed for image processing, and through the application of the K-SVD technique, the dictionaries are trained to adapt effectively to image patches with different properties.

This paper proposed a new image inpainting algorithm based on sparse representation. In traditional exemplar-based methods, the image patch is inpainted by the best matched patch from the source region. This greedy search will introduce unwanted objects and has huge time consuming. The proposed algorithm directly employs all the known image patches to form an over-complete dictionary. And then, the over-complete dictionary is clustered into several sub-dictionaries. Finally, the unrepaired image patches are repaired over their corresponding closest sub-dictionaries through non-negative orthogonal matching pursuit algorithm. Experimental results show that the proposed method achieves superior performance than state-of-the-art methods. In addition, the time complexity is greatly reduced in comparison with the traditional exemplar-based inpainting algorithm.

Image Inpainting Based on Sparse Representation with Dictionary Pre-clustering

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Image Inpainting Based on Sparse Representation with Dictionary Pre-clustering