Image upsampling model. This model upasmple 1080p images into 2160p. The model was trained on only single image. However, it provides substantial performance in different images. Thanks to the generalizable property of convolution.
Model: "model"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input (InputLayer) [(None, 1080, 1920, 3)] 0
conv2d_transpose_1 (Conv2DT (None, 2160, 3840, 8) 38408
ranspose)
conv2d_transpose_2 (Conv2DT (None, 2160, 3840, 16) 51216
ranspose)
conv2d_transpose_3 (Conv2DT (None, 2160, 3840, 16) 25616
ranspose)
conv2d_transpose_4 (Conv2DT (None, 2160, 3840, 3) 1203
ranspose)
tf.math.multiply_1 (TFOpLam (None, 2160, 3840, 3) 0
bda)
tf.__operators__.add_1 (TFO (None, 2160, 3840, 3) 0
pLambda)
=================================================================
Total params: 116,443
Trainable params: 116,443
Non-trainable params: 0
_________________________________________________________________
This Image is the only image that model was trained on:
This image is completely unseen:
