Image super-resolution method based on multi-scale attention convolutional neural network
A convolutional neural network and super-resolution technology, applied in the field of image super-resolution based on multi-scale attention convolutional neural network, can solve problems such as poor results, avoid gradient instability, and improve training and use. Speed, optimized transfer flow effect
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[0035] The invention discloses an image super-resolution method based on a multi-scale attention convolutional neural network, which utilizes the advantages of multi-scale units, attention units, dense connection structures, residual structures, and sub-pixel convolution layers, and can efficiently High-resolution images are reconstructed. Method of the present invention specifically comprises the following steps:
[0036] Step 1, make training set (I LR h HR );
[0037] The present invention uses the DIV2K data set commonly used in the field of image super-resolution reconstruction. The DIV2K data set contains L high-quality images of 2K resolution, including M training sets, N testing sets, and P verification sets, which contain rich The scene can be used to train and test the model. Specifically, first, 16 images are randomly sampled from the DIV2K training dataset, including low-resolution images and corresponding high-resolution images. Then, randomly select image b...
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