mmagic.models.editors.srcnn.srcnn_net
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Module Contents¶
Classes¶
SRCNN network structure for image super resolution. |
- class mmagic.models.editors.srcnn.srcnn_net.SRCNNNet(channels=(3, 64, 32, 3), kernel_sizes=(9, 1, 5), upscale_factor=4)[source]¶
Bases:
mmengine.model.BaseModule
SRCNN network structure for image super resolution.
SRCNN has three conv layers. For each layer, we can define the in_channels, out_channels and kernel_size. The input image will first be upsampled with a bicubic upsampler, and then super-resolved in the HR spatial size.
Paper: Learning a Deep Convolutional Network for Image Super-Resolution.
- Parameters
channels (tuple[int]) – A tuple of channel numbers for each layer including channels of input and output . Default: (3, 64, 32, 3).
kernel_sizes (tuple[int]) – A tuple of kernel sizes for each conv layer. Default: (9, 1, 5).
upscale_factor (int) – Upsampling factor. Default: 4.