RefSR_VAE_Deploy.prototxt

name: "STSR"
input: "LR"
input_dim: 1
input_dim: 3
input_dim: 128
input_dim: 128

input: "Reference"
input_dim: 1
input_dim: 3
input_dim: 128
input_dim: 128


layer{
  name: "noise_data"
  type: "DummyData"
  top: "noise_data"
  dummy_data_param{
    shape {
      dim: 1
      dim: 3
      dim: 128
      dim: 128
    }
    data_filler{
      type: "gaussian"
      value: 1
    }
  }
}

######################### data concatenation #########################
layer {
  name: "concat_data"
  type: "Eltwise"
  bottom: "LR"
  bottom: "Reference"
  top: "concat_data"
  eltwise_param {
    operation: SUM
  }
}

#########################  feature extract #########################
layer {
  name: "feat1_1"
  type: "Convolution"
  bottom: "concat_data"
  top: "feat1_1"
  param {
    lr_mult: 1
    name: "feat1_1_w"
  }
  param {
    lr_mult: 1
    name: "feat1_1_b"
  }
  convolution_param {
    num_output: 64
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.0589
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "Relu_feat1_1"
  type: "ReLU"
  bottom: "feat1_1"
  top: "feat1_1"
}

layer {
  name: "feat1_2"
  type: "Convolution"
  bottom: "feat1_1"
  top: "feat1_2"
  param {
    lr_mult: 1
    name: "feat1_2_w"
  }
  param {
    lr_mult: 1
    name: "feat1_2_b"
  }
  convolution_param {
    num_output: 64
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.0589
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "Relu_feat1_2"
  type: "ReLU"
  bottom: "feat1_2"
  top: "feat1_2"
}

layer {
  name: "feat2_1"
  type: "Convolution"
  bottom: "feat1_2"
  top: "feat2_1"
  param {
    lr_mult: 1
    name: "feat2_1_w"
  }
  param {
    lr_mult: 1
    name: "feat2_1_b"
  }
  convolution_param {
    num_output: 128
    kernel_size: 6
    stride: 2
    pad: 2
    weight_filler {
      type: "gaussian"
      std: 0.0208
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "Relu_feat2_1"
  type: "ReLU"
  bottom: "feat2_1"
  top: "feat2_1"
}

layer {
  name: "feat2_2"
  type: "Convolution"
  bottom: "feat2_1"
  top: "feat2_2"
  param {
    lr_mult: 1
    name: "feat2_2_w"
  }
  param {
    lr_mult: 1
    name: "feat2_2_b"
  }
  convolution_param {
    num_output: 128
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.0417
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "Relu_feat2_2"
  type: "ReLU"
  bottom: "feat2_2"
  top: "feat2_2"
}

layer {
  name: "feat3_1"
  type: "Convolution"
  bottom: "feat2_2"
  top: "feat3_1"
  param {
    lr_mult: 1
    name: "feat3_1_w"
  }
  param {
    lr_mult: 1
    name: "feat3_1_b"
  }
  convolution_param {
    num_output: 256
    kernel_size: 6
    stride: 2
    pad: 2
    weight_filler {
      type: "gaussian"
      std: 0.0147
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "Relu_feat3_1"
  type: "ReLU"
  bottom: "feat3_1"
  top: "feat3_1"
}

layer {
  name: "feat3_2"
  type: "Convolution"
  bottom: "feat3_1"
  top: "feat3_2"
  param {
    lr_mult: 1
    name: "feat3_2_w"
  }
  param {
    lr_mult: 1
    name: "feat3_2_b"
  }
  convolution_param {
    num_output: 256
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.0295
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "Relu_feat3_2"
  type: "ReLU"
  bottom: "feat3_2"
  top: "feat3_2"
}

layer {
  name: "feat4_1"
  type: "Convolution"
  bottom: "feat3_2"
  top: "feat4_1"
  param {
    lr_mult: 1
    name: "feat4_1_w"
  }
  param {
    lr_mult: 1
    name: "feat4_1_b"
  }
  convolution_param {
    num_output: 512
    kernel_size: 6
    stride: 2
    pad: 2
    weight_filler {
      type: "gaussian"
      std: 0.0147
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "Relu_feat4_1"
  type: "ReLU"
  bottom: "feat4_1"
  top: "feat4_1"
}

layer {
  name: "feat4_2"
  type: "Convolution"
  bottom: "feat4_1"
  top: "feat4_2"
  param {
    lr_mult: 1
    name: "feat4_2_w"
  }
  param {
    lr_mult: 1
    name: "feat4_2_b"
  }
  convolution_param {
    num_output: 512
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.0295
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "relu_feat4_2"
  type: "ReLU"
  bottom: "feat4_2"
  top: "feat4_2"
}

######################### Input code #########################
layer {
  name: "input1"
  type: "Convolution"
  bottom: "feat4_2"
  top: "input1"
  param {
    lr_mult: 1
    name: "input1_w"
  }
  param {
    lr_mult: 1
    name: "input1_b"
  }
  convolution_param {
    num_output: 128
    kernel_size: 1
    stride: 1
    pad: 0
    weight_filler {
      type: "gaussian"
      std: 0.125
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "relu_input1"
  type: "ReLU"
  bottom: "input1"
  top: "input1"
}

layer {
  name: "input2"
  type: "Convolution"
  bottom: "input1"
  top: "input2"
  param {
    lr_mult: 1
    name: "input2_w"
  }
  param {
    lr_mult: 1
    name: "input2_b"
  }
  convolution_param {
    num_output: 64
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.0589
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

######################### Encoder #########################
layer {
  name: "encoder1"
  type: "Convolution"
  bottom: "feat4_2"
  top: "encoder1"
  param {
    lr_mult: 1
    name: "encoder1_w"
  }
  param {
    lr_mult: 1
    name: "encoder1_b"
  }
  convolution_param {
    num_output: 256
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.0208
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "Relu_encoder1"
  type: "ReLU"
  bottom: "encoder1"
  top: "encoder1"
}

layer {
  name: "encoder2"
  type: "Convolution"
  bottom: "encoder1"
  top: "encoder2"
  param {
    lr_mult: 1
    name: "encoder2_w"
  }
  param {
    lr_mult: 1
    name: "encoder2_b"
  }
  convolution_param {
    num_output: 128
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.0208
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "Relu_encoder2"
  type: "ReLU"
  bottom: "encoder2"
  top: "encoder2"
}

layer {
  name: "encoder3"
  type: "Convolution"
  bottom: "encoder2"
  top: "encoder3"
  param {
    lr_mult: 1
    name: "encoder3_w"
  }
  param {
    lr_mult: 1
    name: "encoder3_b"
  }
  convolution_param {
    num_output: 128
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.0295
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "Relu_encoder3"
  type: "ReLU"
  bottom: "encoder3"
  top: "encoder3"
}

layer {
  name: "ip1"
  type: "InnerProduct"
  bottom: "encoder3"
  top: "ip1"
  param {
    lr_mult: 1
    decay_mult: 1
    name: "ip1_w"
  }
  param {
    lr_mult: 2
    decay_mult: 0
    name: "ip1_b"
  }
  inner_product_param {
    num_output: 4096
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}

layer {
  name: "relu_ip1"
  type: "ReLU"
  bottom: "ip1"
  top: "ip1"
}

layer {
  name: "ip2"
  type: "InnerProduct"
  bottom: "ip1"
  top: "ip2"
  param {
    lr_mult: 1
    decay_mult: 1
    name: "ip2_w"
  }
  param {
    lr_mult: 2
    decay_mult: 0
    name: "ip2_b"
  }
  inner_product_param {
    num_output: 2048
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}

layer {
  name: "relu_ip2"
  type: "ReLU"
  bottom: "ip2"
  top: "ip2"
}

layer {
  name: "ip3"
  type: "InnerProduct"
  bottom: "ip2"
  top: "ip3"
  param {
    lr_mult: 1
    decay_mult: 1
    name: "ip3_w"
  }
  param {
    lr_mult: 2
    decay_mult: 0
    name: "ip3_b"
  }
  inner_product_param {
    num_output: 2048
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}

layer {
  name: "slicer_ip3"
  type: "Slice"
  bottom: "ip3"
  top: "mu"
  top: "logsd"
  slice_param {
    axis: 1
    slice_point: 1024
  }
}

######################### VAE #########################
layer{
  name: "sd"
  type: "Exp"
  bottom: "logsd"
  top: "sd"
}


######################### decoder #########################
layer {
  name: "generator1"
  type: "InnerProduct"
  bottom: "LR"
  top: "generator1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 512
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}

layer {
  name: "relu_generator1"
  type: "ReLU"
  bottom: "generator1"
  top: "generator1"
}

layer {
  name: "generator2"
  type: "InnerProduct"
  bottom: "generator1"
  top: "generator2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 1024
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}

layer {
  name: "relu_generator2"
  type: "ReLU"
  bottom: "generator2"
  top: "generator2"
}

layer {
  name: "mvn"
  type: "MVN"
  bottom: "generator2"
  top: "generator2"
  mvn_param{
    across_channels: true
    normalize_variance: true
    eps: 1e-9
  }
}

layer {
  name: "reshape_sample"
  type: "Reshape"
  bottom: "generator2"
  top: "norm_generator2"
  reshape_param {
    shape {
      dim: 1
      dim: 1024
    }
  }
}

layer{
  name: "sdnoise"
  type: "Eltwise"
  bottom: "norm_generator2"
  bottom: "sd"
  top: "sdnoise"
  eltwise_param{
    operation: PROD
  }
}

layer{
  name: "sample"
  type: "Eltwise"
  bottom: "mu"
  bottom: "sdnoise"
  top: "sample"
  eltwise_param{
    operation: SUM
  }
}

layer {
  name: "ip4"
  type: "InnerProduct"
  bottom: "sample"
  top: "ip4"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 2048
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}

layer {
  name: "relu_ip4"
  type: "ReLU"
  bottom: "ip4"
  top: "ip4"
}

layer {
  name: "ip5"
  type: "InnerProduct"
  bottom: "ip4"
  top: "ip5"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 2048
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}

layer {
  name: "relu_ip5"
  type: "ReLU"
  bottom: "ip5"
  top: "ip5"
}

layer {
  name: "ip6"
  type: "InnerProduct"
  bottom: "ip5"
  top: "ip6"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 4096
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}

layer {
  name: "relu_ip6"
  type: "ReLU"
  bottom: "ip6"
  top: "ip6"
}

layer {
  name: "reshape"
  type: "Reshape"
  bottom: "ip6"
  top: "ip6_reshape"
  reshape_param {
    shape {
      dim: 1
      dim: 16
      dim: 16
      dim: 16 
    }
  }
}

layer {
  name: "decoder1"
  type: "Convolution"
  bottom: "ip6_reshape"
  top: "decoder1"
  param {
    lr_mult: 1
    name: "decoder1_w"
  }
  param {
    lr_mult: 1
    name: "decoder1_b"
  }
  convolution_param {
    num_output: 64
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.0589
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "relu_decoder1"
  type: "PReLU"
  bottom: "decoder1"
  top: "decoder1"
  prelu_param {
    channel_shared: 1
  }
}

layer{
  name: "decodersum"
  type: "Eltwise"
  bottom: "input2"
  bottom: "decoder1"
  top: "decodersum"
}

######################### SR_s1 #########################
layer {
  name: "conv1_SR_s1"
  type: "Deconvolution"
  bottom: "decodersum"
  top: "conv1_SR_s1"
  param {
    lr_mult: 1
    name: "conv1_s1_w"
  }
  param {
    lr_mult: 1
    name: "conv1_s1_b"
  }
  convolution_param {
    num_output: 32
    kernel_size: 6
    stride: 2
    pad: 2
    weight_filler {
      type: "gaussian"
      std: 0.0417
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "relu1_SR_s1"
  type: "PReLU"
  bottom: "conv1_SR_s1"
  top: "conv1_SR_s1"
  prelu_param {
    channel_shared: 1
  }
}

layer {
  name: "conv2_SR_s1"
  type: "Convolution"
  bottom: "conv1_SR_s1"
  top: "conv2_SR_s1"
  param {
    lr_mult: 1
    name: "conv2_s1_w"
  }
  param {
    lr_mult: 1
    name: "conv2_s1_b"
  }
  convolution_param {
    num_output: 32
    kernel_size: 6
    stride: 2
    pad: 2
    weight_filler {
      type: "gaussian"
      std: 0.0417
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "relu2_SR_s1"
  type: "PReLU"
  bottom: "conv2_SR_s1"
  top: "conv2_SR_s1"
  prelu_param {
    channel_shared: 1
  }
}

layer {
  name: "local_output_SR_s1"
  type: "Convolution"
  bottom: "decodersum"
  top: "local_output_SR_s1"
  param {
    lr_mult: 1
    name: "local_output_s1_w"
  }
  param {
    lr_mult: 1
    name: "local_output_s1_b"
  }
  convolution_param {
    num_output: 32
    kernel_size: 1
    stride: 1
    pad: 0
    weight_filler {
      type: "gaussian"
      std: 0.25
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
    name: "eltwise1_SR_s1"
    type: "Eltwise"
    bottom: "local_output_SR_s1"
    bottom: "conv2_SR_s1"
    top: "eltwise1_SR_s1"
    eltwise_param {
        operation: SUM
        coeff: 1
        coeff: -1
    }
}

layer {
  name: "conv3_SR_s1"
  type: "Deconvolution"
  bottom: "eltwise1_SR_s1"
  top: "conv3_SR_s1"
  param {
    lr_mult: 1
    name: "conv3_s1_w"
  }
  param {
    lr_mult: 1
    name: "conv3_s1_b"
  }
  convolution_param {
    num_output: 32
    kernel_size: 6
    stride: 2
    pad: 2
    weight_filler {
      type: "gaussian"
      std: 0.0417
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "relu3_SR_s1"
  type: "PReLU"
  bottom: "conv3_SR_s1"
  top: "conv3_SR_s1"
  prelu_param {
    channel_shared: 1
  }
}

layer {
  name: "local_conv1_SR_s1"
  type: "Convolution"
  bottom: "conv1_SR_s1"
  top: "local_conv1_SR_s1"
  param {
    lr_mult: 1
    name: "local_conv1_s1_w"
  }
  param {
    lr_mult: 1
    name: "local_conv1_s1_b"
  }
  convolution_param {
    num_output: 32
    kernel_size: 1
    stride: 1
    pad: 0
    weight_filler {
      type: "gaussian"
      std: 0.25
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
    name: "eltwise2_SR_s1"
    type: "Eltwise"
    bottom: "local_conv1_SR_s1"
    bottom: "conv3_SR_s1"
    top: "output_SR_s1"
    eltwise_param {
        operation: 1
    }
}

layer {
  name: "relu_output_SR_s1"
  type: "PReLU"
  bottom: "output_SR_s1"
  top: "output_SR_s1"
  prelu_param {
    channel_shared: 1
  }
}

######################### SR_s2 #########################
layer {
  name: "conv1_SR_s2"
  type: "Deconvolution"
  bottom: "output_SR_s1"
  top: "conv1_SR_s2"
  param {
    lr_mult: 1
    name: "conv1_s2_w"
  }
  param {
    lr_mult: 1
    name: "conv1_s2_b"
  }
  convolution_param {
    num_output: 32
    kernel_size: 6
    stride: 2
    pad: 2
    weight_filler {
      type: "gaussian"
      std: 0.0417
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "relu1_SR_s2"
  type: "PReLU"
  bottom: "conv1_SR_s2"
  top: "conv1_SR_s2"
  prelu_param {
    channel_shared: 1
  }
}

layer {
  name: "conv2_SR_s2"
  type: "Convolution"
  bottom: "conv1_SR_s2"
  top: "conv2_SR_s2"
  param {
    lr_mult: 1
    name: "conv2_s2_w"
  }
  param {
    lr_mult: 1
    name: "conv2_s2_b"
  }
  convolution_param {
    num_output: 32
    kernel_size: 6
    stride: 2
    pad: 2
    weight_filler {
      type: "gaussian"
      std: 0.0417
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "relu2_SR_s2"
  type: "PReLU"
  bottom: "conv2_SR_s2"
  top: "conv2_SR_s2"
  prelu_param {
    channel_shared: 1
  }
}

layer {
  name: "local_output_SR_s2"
  type: "Convolution"
  bottom: "output_SR_s1"
  top: "local_output_SR_s2"
  param {
    lr_mult: 1
    name: "local_output_SR_s2_w"
  }
  param {
    lr_mult: 1
    name: "local_output_SR_s2_b"
  }
  convolution_param {
    num_output: 32
    kernel_size: 1
    stride: 1
    pad: 0
    weight_filler {
      type: "gaussian"
      std: 0.25
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
    name: "eltwise1_SR_s2"
    type: "Eltwise"
    bottom: "local_output_SR_s2"
    bottom: "conv2_SR_s2"
    top: "eltwise1_SR_s2"
    eltwise_param {
        operation: SUM
        coeff: 1
        coeff: -1
    }
}

layer {
  name: "conv3_SR_s2"
  type: "Deconvolution"
  bottom: "eltwise1_SR_s2"
  top: "conv3_SR_s2"
  param {
    lr_mult: 1
    name: "conv3_s2_w"
  }
  param {
    lr_mult: 1
    name: "conv3_s2_b"
  }
  convolution_param {
    num_output: 32
    kernel_size: 6
    stride: 2
    pad: 2
    weight_filler {
      type: "gaussian"
      std: 0.0417
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "relu3_SR_s2"
  type: "PReLU"
  bottom: "conv3_SR_s2"
  top: "conv3_SR_s2"
  prelu_param {
    channel_shared: 1
  }
}

layer {
  name: "local_conv1_SR_s2"
  type: "Convolution"
  bottom: "conv1_SR_s2"
  top: "local_conv1_SR_s2"
  param {
    lr_mult: 1
    name: "local_conv1_s2_w"
  }
  param {
    lr_mult: 1
    name: "local_conv1_s2_b"
  }
  convolution_param {
    num_output: 32
    kernel_size: 1
    stride: 1
    pad: 0
    weight_filler {
      type: "gaussian"
      std: 0.25
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
    name: "eltwise2_SR_s2"
    type: "Eltwise"
    bottom: "local_conv1_SR_s2"
    bottom: "conv3_SR_s2"
    top: "output_SR_s2"
    eltwise_param {
        operation: 1
    }
}

layer {
  name: "relu_output_SR_s2"
  type: "PReLU"
  bottom: "output_SR_s2"
  top: "output_SR_s2"
  prelu_param {
    channel_shared: 1
  }
}

######################### SR_s3 #########################
layer {
  name: "conv1_SR_s3"
  type: "Deconvolution"
  bottom: "output_SR_s2"
  top: "conv1_SR_s3"
  param {
    lr_mult: 1
    name: "conv1_s3_w"
  }
  param {
    lr_mult: 1
    name: "conv1_s3_b"
  }
  convolution_param {
    num_output: 32
    kernel_size: 6
    stride: 2
    pad: 2
    weight_filler {
      type: "gaussian"
      std: 0.0417
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "relu1_SR_s3"
  type: "PReLU"
  bottom: "conv1_SR_s3"
  top: "conv1_SR_s3"
  prelu_param {
    channel_shared: 1
  }
}

layer {
  name: "conv2_SR_s3"
  type: "Convolution"
  bottom: "conv1_SR_s3"
  top: "conv2_SR_s3"
  param {
    lr_mult: 1
    name: "conv2_s3_w"
  }
  param {
    lr_mult: 1
    name: "conv2_s3_b"
  }
  convolution_param {
    num_output: 32
    kernel_size: 6
    stride: 2
    pad: 2
    weight_filler {
      type: "gaussian"
      std: 0.0417
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "relu2_SR_s3"
  type: "PReLU"
  bottom: "conv2_SR_s3"
  top: "conv2_SR_s3"
  prelu_param {
    channel_shared: 1
  }
}

layer {
  name: "local_output_SR_s3"
  type: "Convolution"
  bottom: "output_SR_s2"
  top: "local_output_SR_s3"
  param {
    lr_mult: 1
    name: "local_output_SR_s3_w"
  }
  param {
    lr_mult: 1
    name: "local_output_SR_s3_b"
  }
  convolution_param {
    num_output: 32
    kernel_size: 1
    stride: 1
    pad: 0
    weight_filler {
      type: "gaussian"
      std: 0.25
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
    name: "eltwise1_SR_s3"
    type: "Eltwise"
    bottom: "local_output_SR_s3"
    bottom: "conv2_SR_s3"
    top: "eltwise1_SR_s3"
    eltwise_param {
        operation: SUM
        coeff: 1
        coeff: -1
    }
}

layer {
  name: "conv3_SR_s3"
  type: "Deconvolution"
  bottom: "eltwise1_SR_s3"
  top: "conv3_SR_s3"
  param {
    lr_mult: 1
    name: "conv3_s3_w"
  }
  param {
    lr_mult: 1
    name: "conv3_s3_b"
  }
  convolution_param {
    num_output: 32
    kernel_size: 6
    stride: 2
    pad: 2
    weight_filler {
      type: "gaussian"
      std: 0.0417
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "relu3_SR_s3"
  type: "PReLU"
  bottom: "conv3_SR_s3"
  top: "conv3_SR_s3"
  prelu_param {
    channel_shared: 1
  }
}

layer {
  name: "local_conv1_SR_s3"
  type: "Convolution"
  bottom: "conv1_SR_s3"
  top: "local_conv1_SR_s3"
  param {
    lr_mult: 1
    name: "local_conv1_s3_w"
  }
  param {
    lr_mult: 1
    name: "local_conv1_s3_b"
  }
  convolution_param {
    num_output: 32
    kernel_size: 1
    stride: 1
    pad: 0
    weight_filler {
      type: "gaussian"
      std: 0.25
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
    name: "eltwise2_SR_s3"
    type: "Eltwise"
    bottom: "local_conv1_SR_s3"
    bottom: "conv3_SR_s3"
    top: "output_SR_s3"
    eltwise_param {
        operation: 1
    }
}

layer {
  name: "relu_output_SR_s3"
  type: "PReLU"
  bottom: "output_SR_s3"
  top: "output_SR_s3"
  prelu_param {
    channel_shared: 1
  }
}

######################### Reconstruction #########################
layer {
  name: "SR_res"
  type: "Convolution"
  bottom: "output_SR_s3"
  top: "SR_res"
  param {
    lr_mult: 1
    name: "SR_res_w"
  }
  param {
    lr_mult: 1
    name: "SR_res_b"
  }
  convolution_param {
    num_output: 3
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.2722
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "SR"
  type: "Eltwise"
  bottom: "LR"
  bottom: "SR_res"
  top: "SR"
  eltwise_param {
      operation: 1
  }
}