Thanks Jerome. So it's not feasible to realistically reduce the stride.

I hope this similar topic will also interest you. What about sliding this denoising autoencoder?

size = 157;

n1 = 32;
k = 5;

conv2[n_] := 
  NetChain[{ConvolutionLayer[n, k, "Stride" -> 2], 
    BatchNormalizationLayer[], ElementwiseLayer["ReLU"], 
    DropoutLayer[], ConvolutionLayer[n, k, "Stride" -> 2], 
    BatchNormalizationLayer[], ElementwiseLayer["ReLU"]}];

deconv2[n_] := 
  NetChain[{DeconvolutionLayer[n, k, "Stride" -> 2], 
    BatchNormalizationLayer[], ElementwiseLayer["ReLU"], 
    DropoutLayer[], DeconvolutionLayer[n/2, k, "Stride" -> 2], 
    BatchNormalizationLayer[], ElementwiseLayer["SoftSign"]}];

sum[] := NetChain[{TotalLayer["Inputs" -> 2]}];

constantPowerLayer[] := NetChain[{
   ElementwiseLayer[Log@Clip[#, {$MachineEpsilon, 1}] &],
   ConvolutionLayer[1, 1, "Biases" -> None, "Weights" -> {{{{1}}}}],
   ElementwiseLayer[Exp]}]

ddae = NetGraph[
  <|
   "bugworkaround" -> ElementwiseLayer[# &],
   "c12" -> conv2[n1],
   "c34" -> conv2[2*n1],

   "d12" -> deconv2[2*n1],
   "d34" -> 
    NetChain[{DeconvolutionLayer[n1, k, "Stride" -> 2], 
      BatchNormalizationLayer[], ElementwiseLayer["ReLU"], 
      DeconvolutionLayer[1, k, "Stride" -> 2], 
      BatchNormalizationLayer[], ElementwiseLayer["SoftSign"]}],

   "sum1" -> sum[],
   "sum2" -> NetChain[{sum[], constantPowerLayer[]}],

   "loss" -> MeanSquaredLossLayer[]
   |>,
  {
   "bugworkaround" -> 
    "c12" -> 
     "c34" -> 
      "d12" -> "sum1" -> "d34" -> "sum2" -> NetPort["loss", "Input"],
   "bugworkaround" -> "sum2",
   NetPort["Noisy"] -> "bugworkaround",
   "c12" -> "sum1",
   NetPort["Target"] -> NetPort["loss", "Target"]
   },
  "Noisy" -> 
   NetEncoder[{"Image", {size, size}, ColorSpace -> "Grayscale"}],
  "Target" -> 
   NetEncoder[{"Image", {size, size}, ColorSpace -> "Grayscale"}]
  ]

trained = NetTake[NetInitialize@ddae, {"bugworkaround", "sum2"}]

Now I "automatize" the input dimensions:

n3 = NetReplacePart[trained, "Noisy" -> Automatic];

This new network works fine if given same dimensions, but fails with larger input dimensions. Any idea how to fix this problem?

In[142]:= n3[RandomReal[1, {1, 157, 157}]] // Dimensions

Out[142]= {1, 157, 157}

In[143]:= n3[RandomReal[1, {1, 1570, 1570}]] // Dimensions

During evaluation of In[143]:= NetGraph::tyfail1: Inferred inconsistent value for output size of layer 4 of layer "d34".

Out[143]= {}

Ah yes. Thanks! This is used in my follow up question.