# Image Error Concealment Based on Deep Neural Network
## 1 Introduction
The estimator employs a probability density function obtained by kernel density estimation (KDE). More recently, [PERSON] et al. [PERSON] et al. (2018) reconstructed missing pixels through an adaptive linear predictor (ALP). The predictor can automatically tune its order and support shape according to the local contexts. In addition, they proposed an uncertainty-based scan order to alleviate the error propagation problem. However, they only considered the uncertainty of the neighbor pixels of the missing pixels, while the credibility of the reconstruction also influences the error propagation. In [PERSON] et al. (2019), over-completed dictionaries were learned for recovering missing pixels. They trained two dictionaries individually for the missing part and the available part, and they reconstructed the missing part through a local correlation model that was trained to bridge the two dictionaries. In [PERSON] et al. (2019), they proposed a Gaussian-weighted non-local texture similarity measure to obtain multiple candidate patches for each target patch, using the non-local texture measure. Therefore, their algorithm is capable of reproducing the underlying textural details. Neural network, as a powerful model, has been proved to be effective in EC tasks [PERSON] et al. (2019); [PERSON] et al. (2019); [PERSON] and [PERSON] [PERSON] and [PERSON] (2019). [PERSON] and [PERSON] [PERSON] and [PERSON] (2019) exploited a general regression neural network (GRNN) to estimate the motion vectors of the corrupted macro-blocks (MBs). They collected the adjacent motion vectors of the corrupted MBs for training the GRNN at first. Then, the corrupted MBs were reconstructed through the corresponding motion vectors, which were estimated by the GRNN. In [PERSON] et al. (2019), deep neural networks were used to predict the optical flow for video error concealment. They designed two parallel networks to separately process the horizontal and vertical motion fields of the optical flows of the previous frame. Then, the combined output of the two networks was used to reconstruct the corrupted portion of the video frames. Both of the aforementioned two methods reconstructed corrupted images by utilizing the information from the adjacent frames of the video. However, reliable adjacent frame information is not available in some situations. For example, corrupted images are not from videos. Unlike [PERSON] et al. (2019); [PERSON] and [PERSON] (2019), we reconstructed the missing pixels through only utilizing the information from the current frame in this paper. We propose a novel EC technique to reconstruct the missing pixels via training a deep neural network. Considering that the natural images have local correlation and non-local self-similarity, we used the local information to predict the missing pixels and the non-local information to correct the predictions. The designed neural network can be divided into two parts: the prediction part and the auto-encoder (AE) part. The first part utilizes the local correlation among pixels to predict the missing ones. The other part extracts image features, which are used to collect similar samples from the whole image. In addition, we propose a novel adaptive scan order based on the joint credibility of the support area and reconstruction to alleviate the error propagation problem. The experimental results show that the proposed algorithm can reconstruct corrupted images effectively and outperforms the compared state-of-the-art methods in terms of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM).