super-resolution

I am working on a program that works on hyperspectral image super-resolution by using Neural Networks, Now in here the Mains directory of the program contains multiple parsers. The parsers and subparsers seem to have been defined correctly

I'm having a problem implementing a super-resolution model

I am trying to implement GANs for super-resolution enhancement using Tensorflow. Here is the link of my collab code https://colab.research.google.com/drive/1F376Y6T5ehoE-mk2q7LGBmLq-OkWxqtr?usp=sharing

First image is the output image

Second is the Expected output

Can someone help me figure out what is wrong in the image. Any help would be appreciated. Thank you

I have implemented and trained the model from the following website, and using the author's source code:

I am now running an image through the trained network and want to get the network output (feature maps etc.) at every stage.

To that end, I have tried making sub-models from groups of layers from the full model (called sizedModel in my code) and checking their output.

I have done that for the first L1(Conv2D)

I am trying to merge two trained neural networks. I have two trained Keras model files A and B.

Model A is for image super-resolution and model B is for image colorization.

I am trying to merge two trained networks so that I can inference SR+colorization faster. (I am not willing to use a single network to accomplish both SR and colorization tasks. I need to use two different networks for SR and colorization tasks.)

Any tips on how to merge two Keras neural networks?

i am Python beginner.

I succeeded in using "image enhancing(from tensorflow lib)" running on colab and I wanted to run it on jupyter notebook, so I moved it.

enter image description here

Testing ONNX model for super resolution model, I have error running this sample program.

My ONNX version is 1.5.0 with onnxruntime 1.4.0. Onnxruntime was installed using pip. Pytorch version is 1.6.0

Error is at ort_session = onnxruntime.InferenceSession('/home/itc/pytorch/sub_pixel_cnn_2016/model/super-resolution-10.onnx')

The error is in loading onnx model.

I recently finished the Image super-resolution using Autoencoders in Coursera and when I try to run the same code on my laptop using Spyder and Jupyter notebook, I keep getting this error. I am using Nvidia GeForce 1650Ti along with Tensorflow-gpu=2.3.0, CUDA=10.1, cuDNN=7.6.5 and python=3.8.5. I have used the same configurations for running many deep neural network problems and none of them gave this error.

Code:

https://towardsdatascience.com/deep-learning-based-super-resolution-with-opencv-4fd736678066

this is the code copied out of the article;

import cv2 from cv2 import dnn_superres

sr = dnn_superres.DnnSuperResImpl_create()

image = cv2.imread('./input.png')

path = "EDSR_x3.pb" sr.readModel(path)

sr.setModel("edsr", 3)

result = sr.upsample(image)

cv2.imwrite("./upscaled.png", result)

https://docs.opencv.org/master/d5/d29/tutorial_dnn_superres_upscale_image_single.html

import cv2 from cv2 import dnn_superres

sr = dnn_superres.DnnSuperResImpl_create()

image = cv2.imread('./image.png')

path = "EDSR_x4.pb" sr.readModel(path)

sr.setModel("edsr", 4)

result = sr.upsample(image)

cv2.imwrite("./upscaled.png", result)

My enviroment is anaconda3 opencv 4.3.0. I either get the error from the title or i get "killed" when i run the opencv example.

*My file directory is all on the same level of the sample codes. I would just change my image file names. I did try to compile opencv and opencv_contrib from cmake but, i didn't know how to have python refer to opencv and opencv_contrib from source. ifollow this documentation to install opencv from source; https://d*ocs.opencv.org/3.4/d2/de6/tutorial_py_setup_in_ubuntu.html

I opted to use anaconda wrapping of opencv 4.3.0 because i ran into too many dependency and wrongly installed package problems.

My friend from a meetup managed to apply the code from the article just as the article depicted while i tried to follow exactly what he did, using an anaconda enviorment. Would my problem stem from my virtual enviorment or opencv package version or the code itself? i did have another colleague run my code from my github branch and he had my exact same problems. How should i apprach the bugs im having and apply the super resolution examples i found?