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- W3043014596 abstract "Image inpainting is usually framed as a constrained image generation problem. It is a method that helps to reconstruct the lost or deteriorated parts of images as well as video. The main focus in image inpainting technique is how precisely to generate the corrupted pixels in an image. Traditional inpainting algorithms are unfortunately not well adapted to handle such corruptions as they rely on image processing techniques that cannot properly infer missing information when the corrupted holes are too large. In this paper, a single pass method of inpaintng method is used which does not require any back propagation for training purpose, hence saving time for training the model. The objective of the proposed model is to reconstruct large continuous regions of missing or deteriorated parts of an image. In this paper, role of Extreme Machine Learning is a single pass neural network model, where we train our model based on region surrounding the corrupted region. Each image is divided into two sections: the missing part that we try to reconstruct, and the context. The network does not require any region to be in some defined shape as it can also work on arbitrary regions. Final evaluation is based on the average element-wise L2 distance between the corrupted image and the original image for the regions which is to be regenerate. We have also used PSNR value for comparison between original image and the image which we reconstruct." @default.
- W3043014596 created "2020-07-23" @default.
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- W3043014596 date "2020-07-19" @default.
- W3043014596 modified "2023-09-25" @default.
- W3043014596 title "Image Inpainting for Irregular Holes Using Extreme Learning Machine" @default.
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- W3043014596 doi "https://doi.org/10.1007/978-3-030-50641-4_5" @default.
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