Keras Concatenate() error

I’m trying to make a UNet based Autoencoder model to De-Blur images. I defined the model as follows:

def conv_operation(x, filters, kernel_size, strides=2):
x = Conv2D(filters=filters,
          kernel_size=kernel_size,
          strides=strides,
          padding='same')(x)
x = BatchNormalization()(x)
x = ReLU()(x)
return x

def conv_transpose_operation(x, filters, kernel_size):
x = Conv2DTranspose(filters=filters,
                   kernel_size=kernel_size,
                   strides=2,
                   padding='same')(x)
x = BatchNormalization()(x)
x = ReLU()(x)
return x

def deblurring_autoencoder():
    dae_inputs = Input(shape=(200,200, 3), name='dae_input')
    conv_block1 = conv_operation(dae_inputs, 32, 3)
    conv_block2 = conv_operation(conv_block1, 64, 3)
    conv_block3 = conv_operation(conv_block2, 128, 3)
    conv_block4 = conv_operation(conv_block3, 256, 3)

    conv_block5 = conv_operation(conv_block4, 256, 3, 1)

    deconv_block1 = conv_transpose_operation(conv_block5, 256,3)
    merge1 = Concatenate()([conv_block3,deconv_block1])
    deconv_block2 = conv_transpose_operation(merge1, 128, 2)
    merge2 = Concatenate()([deconv_block2, conv_block2])
    deconv_block3 = conv_transpose_operation(merge2, 64, 3)
    merge3 = Concatenate()([deconv_block3, conv_block1])
    deconv_block4 = conv_transpose_operation(merge3, 32, 3)

    final_deconv = Conv2DTranspose(filters=3, kernel_size=3)(deconv_block4)

    dae_outputs = Activation('sigmoid', name='dae_output')(final_deconv)

    return Model(dae_inputs, dae_outputs, name='dae')

After these definitons, I try to make the model like follows:

model= deblurring_autoencoder()

When I run the above line, I get a long error that basically tells me that my code is breaking at the line:

merge1 = Concatenate()([conv_block3,deconv_block1])

due to a dimensionality error. The error says:

A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got inputs shapes: [(None, 25, 25, 128), (None, 26, 26, 256)]

I tried to manually check all the dimensions after each convolution and they seem to fit perfectly for me. One things I noticed is that whenever i take the input shape in the Input() function as (32,32,3) or (64,64,3) or (128,128,3), etc, I get no errors.
How can I resolve this?