Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Most of existing inpainting techniques require to know beforehandwhere those damaged pixels are, i.e., non-blind inpainting methods. Hover, in many applications, such information may not be readily available. ,  propose a novel blind inpainting method based on a fully convolutional neural network.  term this method as blind inpainting convolutional neural network (BICNN). It purely cascades three convolutional layers to directly learn an end-to-end mapping beten a pre-acquired dataset of corrupted/ground truth subimage pairs. Stochastic gradient descent with standard backpropagation is used to train the BICNN. Once the BICNN is learned, it can automatically identify and remove the corrupting patterns from a corrupted image without knowing the specific regions. The learned BICNN takes a corrupted image of any size as input and directly produces a clean output by only one pass of forward propagation. Experimental results indicate that the proposed method can achieve a better inpainting performance than the existing inpainting methods for various corrupting patterns.
['Nian Cai', 'Zhenghang Su', 'Zhineng Lin', 'Han Wang', 'Zhijing Yang', 'Bingo Wing-Kuen Ling']
I am writing a research paper on a idea Blind inpainting using the fully convolutional neural network
[{"from": "human", "value": "I am writing a research paper on a idea Blind inpainting using the fully convolutional neural network"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Most of existing inpainting techniques require to know beforehandwhere those damaged pixels are, i.e., non-blind inpainting methods. Hover, in many applications, such information may not be readily available. ,  propose a novel blind inpainting method based on a fully convolutional neural network.  term this method as blind inpainting convolutional neural network (BICNN). It purely cascades three convolutional layers to directly learn an end-to-end mapping beten a pre-acquired dataset of corrupted/ground truth subimage pairs. Stochastic gradient descent with standard backpropagation is used to train the BICNN. Once the BICNN is learned, it can automatically identify and remove the corrupting patterns from a corrupted image without knowing the specific regions. The learned BICNN takes a corrupted image of any size as input and directly produces a clean output by only one pass of forward propagation. Experimental results indicate that the proposed method can achieve a better inpainting performance than the existing inpainting methods for various corrupting patterns."}]