Although tremendous advancement happen in image processing domain, still “filling the missing areas” is area of concern in it. Though lot of progress has been made in the past years, still lot of work should be done. A novel algorithm is presented for examplar-based inpainting. In the proposed algorithm initially inpainting is applied on the coarse version of the input image, latter hierarchica...

In this work we propose Pixel Content Encoders (PCE), a lightweight image inpainting model, capable of generating novel content for large missing regions in images. Unlike previously presented convolutional neural network based models, our PCE model has an order of magnitude fewer trainable parameters. Moreover, by incorporating dilated convolutions we are able to preserve fine grained spatial ...

A regular convolution layer applying a filter in the same way over known and unknown areas causes visual artifacts inpainted image. Several studies address this issue with feature re-normalization on output of convolution. However, these models use significant amount learnable parameters for [41, 48], or assume binary representation certainty an [11, 26]. We propose (layer-wise) imputation miss...

In this paper, we present a novel image inpainting technique using frequency domain information. Prior works on predict the missing pixels by training neural networks only spatial However, these methods still struggle to reconstruct high-frequency details for real complex scenes, leading discrepancy in color, boundary artifacts, distorted patterns, and blurry textures. To alleviate problems, in...

Image inpainting refers to filling in unknown regions with known knowledge, which is full flourish accompanied by the popularity and prosperity of deep convolutional networks. Current methods have excelled completing small-sized corruption or specifically masked images. However, for large-proportion corrupted images, most attention-based structure-based approaches, though reported state-of-the-...

In this paper, we present an image inpainting algorithm based on framelet analysis. The motivation for using framelets is that the redundancy provided by the framelets makes it possible to propagate the accurate information from the vicinity of the region to be inpainted to the inside of the region to be inpainted. This is done by small perturbations of the framelet coefficients via thresholdin...