Fast and Full-Resolution Light Field Deblurring Using a Deep Neural Network
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...This includes tasks such as spatial super-resolution [24]–[27], deblurring [28]–[30], denoising [31]–[35], and depth estimation [7]–[9]....
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References
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"Fast and Full-Resolution Light Fiel..." refers methods in this paper
...We draw on a previous study [9] by predicting and adding the residual image with the input to produce the deblurred result....
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3,118 citations
"Fast and Full-Resolution Light Fiel..." refers methods in this paper
...The residual blocks follow the traditional ResNet model [7] and simple modification is made by applying instance normalization [26] instead of batch normalization to normalize the features’ contrasts....
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...This strategy is supported by applying instance normalization [26] and ReLU activation on every 2D convolution layer, except for the last layer 2D Conv4, which utilizes Tanh activation without any normalization....
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1,560 citations
"Fast and Full-Resolution Light Fiel..." refers background or methods in this paper
...Network Architecture We use the combination of convolution-deconvolutional and residual styles on the network, which is proven to produce satisfying results on image deblurring [10, 17]....
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...Recent blur datasets are only available for 2D or 3D image (video) deblurring [10, 17, 24]....
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...Earlier works have achieved state of the art performance for deblurring 2D images and 3D videos [10, 17, 24]....
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...These recent works apply image deblurring directly without blur kernel estimation [10, 12, 17, 24]....
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1,489 citations
"Fast and Full-Resolution Light Fiel..." refers background in this paper
...[15] introduced a sparse derivative prior that concentrates on the derivatives of low intensity pixels....
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