Application of GRNN neural network in non-texture image inpainting and restoration

We model an inpainting method based on GRNN neural network.The missing regions are separated and sorted according to their size.The missing regions are determined by performing regression analysis.We use the magnitude of the gradient of the image to determine the spread parameter.For color images, the YCbCr color space is employed. Inspired by the connectivity principle of human visual perception, a new inpainting approach based on GRNN neural network is proposed in this paper. The missing regions in this new technique are determined by performing regression analysis on the image data. The missing regions are first separated and sorted according to their size. Then the algorithm proceeds with applying a GRNN network to each one in order to repair their damaged pixels. Simplicity and efficiency are the main advantages of the proposed approach. The performance of the proposed approach is evaluated in three application contexts: text removal, scratch removal, and noise removal. Where possible, we used objective measures (e.g., PSNR) to evaluate the visual quality of the inpainted images. The results demonstrate the effectiveness of the proposed approach. Display Omitted

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