An end-to-end deep learning framework for super-resolution based inpainting

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Abstract

Image inpainting is an extremely challenging and open problem for the computer vision community. Motivated by the recent advancement in deep learning algorithms for computer vision applications, we propose a new end-to-end deep learning based framework for image inpainting. Firstly, the images are down-sampled as it reduces the targeted area of inpainting therefore enabling better filling of the target region. A down-sampled image is inpainted using a trained deep convolutional auto-encoder (CAE). A coupled deep convolutional auto-encoder (CDCA) is also trained for natural image super resolution. The pre-trained weights from both of these networks serve as initial weights to an end-to-end framework during the fine tuning phase. Hence, the network is jointly optimized for both the aforementioned tasks while maintaining the local structure/information. We tested this proposed framework with various existing image inpainting datasets and it outperforms existing natural image blind inpainting algorithms. Our proposed framework also works well to get noise resilient super-resolution after fine-tuning on noise-free super-resolution dataset. It provides more visually plausible and better resultant image in comparison of other conventional and state-of-the-art noise-resilient super-resolution algorithms.

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Sharma, M., Mukhopadhyay, R., Chaudhury, S., & Lall, B. (2018). An end-to-end deep learning framework for super-resolution based inpainting. In Communications in Computer and Information Science (Vol. 841, pp. 198–208). Springer Verlag. https://doi.org/10.1007/978-981-13-0020-2_18

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