Body: <p>I have a 1D signal that will produce a 2D image after some image processing algorithm. Would it be possible and effective to use deep learning method to reproduce the 1D signal if I have the 2D image instead? If yes, what kind of deep learning neural network will be effective in this case?</p>
  <p>In my application, I am looking at the noise images of the 2D synthetic aperture radar (SAR) images. Each image should look like random noise image (eg. tv static noise). I have the image processing algorithm that enables me to convert a 1D (or even a 2D) complex signal into that noise image. I am curious if it is possible for me to do the reverse process using deep learning instead because reversing the image processing algorithm is a complicated task. For dataset wise, I will randomly generate the 1D (or 2D) complex signals as the ground truths and the noise image as the training data. With the image processing algorithm, there should be no issue in generating the datasets.</p>
  <p>Is there any relevant research / Github working on this method?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-01-04T10:33:45.743Z
Comments.Text: Can you give more details about your specific problem? Why do you want to convert the image to the 1d signal? What task are you trying to solve? What have you found so far?
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-01-04T15:18:26.467Z
Comments.Text: Thanks for the reply. I am trying to retrieve a 1D pulsed signal (radar) from a 2D synthetic aperture radar (SAR) image. Nothing has been done on my part yet. I am merely trying to establish a proof of concept.
Comments.UserDisplayName: GordonJun
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-01-04T15:38:15.190Z
Comments.Text: I am not familiar with "2D synthetic aperture radar (SAR) images", but if there are patterns that can be used to convert the 2d images into 1d signals, it may be possible to do it with machine learning. I think it could help if you show us an image of "a 2D synthetic aperture radar (SAR)" and what you wish to obtain from that image, i.e. what exactly would be the 1d signal that you expect. Having said that, remember that ML is about learning from data. So, how much data do you have? Do you have labelled data?
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-01-04T17:21:36.063Z
Comments.Text: In my application, I am looking at the noise images of the 2D synthetic aperture radar (SAR) images. Each image should look like random noise image (eg. tv static noise). I have the image processing algorithm that enables me to convert a 1D (or even a 2D) complex signal into that noise image. I am curious if it is possible for me to do the reverse process using deep learning instead because reversing the image processing algorithm is a complicated task. For dataset wise, I will randomly generate the 1D (or 2D) complex signals as the ground truths and the noise image as the training data.
Comments.UserDisplayName: GordonJun
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-01-05T10:07:54.813Z
Comments.Text: I would recommend that you [edit your post](https://ai.stackexchange.com/posts/34018/edit) include these details because comments are temporary and are only meant to ask for clarification. In addition, after editing, your question will pop up as an active post and people will maybe read it again.
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-01-05T11:15:09.977Z
Comments.Text: One thing that I think is still missing from your description in the post is how the image processing algorithm that converts 1d to 2d noise images works. If your 2d images are just noise and there's no pattern in them, I doubt you can do what you want. It may also be a good idea to provide the example of a 2d image that you have in mind and the corresponding 1d signal. Just to have a better idea of the types of inputs and outputs that you have.
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-01-05T16:22:02.677Z
Comments.Text: @nbro, yes thanks for the suggestions. I will edit accordingly.
Comments.UserDisplayName: GordonJun
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-01-24T08:09:58.737Z
Comments.Text: I am working on the same problem like 2d image to 1d signal. I also generated a 2d synthetic image from a 1d signal in plasma science and now try to reverse this. Is there any deep learning modal that can help with this? Please guild me.
Comments.UserDisplayName: Dhruvil Gorasiya
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-01-25T09:44:56.310Z
Comments.Text: @DhruvilGorasiya Hi there. It has been quite a while. Eventually, I decided to make use of this repository in GitHub https://github.com/LLNL/macc. The research was also built upon a plasma physics problem. The main idea of the deep learning model is to recreate a 2D image from a set of parameters. To do this, a subnetwork is trained to take in a 2D image and then convert it into a 1D latent space before converting back in the 2D image. Although this may not be specifically what you need, I hope this can give you a direction.
Comments.UserDisplayName: GordonJun
Tags: <deep-learning><image-processing><algorithm-request><model-request><signal-processing>
Title: Is it effective to use deep learning method to produce a 1D signal as output from a 2D image as input?
UserDisplayName: GordonJun
ThreadText: # Is it effective to use deep learning method to produce a 1D signal as output from a 2D image as input?
  
  (Asked by: username_0 on 2022-01-04)
  
  I have a 1D signal that will produce a 2D image after some image processing algorithm. Would it be possible and effective to use deep learning method to reproduce the 1D signal if I have the 2D image instead? If yes, what kind of deep learning neural network will be effective in this case?
  
  In my application, I am looking at the noise images of the 2D synthetic aperture radar (SAR) images. Each image should look like random noise image (eg. tv static noise). I have the image processing algorithm that enables me to convert a 1D (or even a 2D) complex signal into that noise image. I am curious if it is possible for me to do the reverse process using deep learning instead because reversing the image processing algorithm is a complicated task. For dataset wise, I will randomly generate the 1D (or 2D) complex signals as the ground truths and the noise image as the training data. With the image processing algorithm, there should be no issue in generating the datasets.
  
  Is there any relevant research / Github working on this method?
  
  **Tags:** deep-learning, image-processing, algorithm-request, model-request, signal-processing
  > **Comments:**
  > **username_1**: Can you give more details about your specific problem? Why do you want to convert the image to the 1d signal? What task are you trying to solve? What have you found so far?  
  > **username_0**: Thanks for the reply. I am trying to retrieve a 1D pulsed signal (radar) from a 2D synthetic aperture radar (SAR) image. Nothing has been done on my part yet. I am merely trying to establish a proof of concept.  
  > **username_1**: I am not familiar with "2D synthetic aperture radar (SAR) images", but if there are patterns that can be used to convert the 2d images into 1d signals, it may be possible to do it with machine learning. I think it could help if you show us an image of "a 2D synthetic aperture radar (SAR)" and what you wish to obtain from that image, i.e. what exactly would be the 1d signal that you expect. Having said that, remember that ML is about learning from data. So, how much data do you have? Do you have labelled data?  
  > **username_0**: In my application, I am looking at the noise images of the 2D synthetic aperture radar (SAR) images. Each image should look like random noise image (eg. tv static noise). I have the image processing algorithm that enables me to convert a 1D (or even a 2D) complex signal into that noise image. I am curious if it is possible for me to do the reverse process using deep learning instead because reversing the image processing algorithm is a complicated task. For dataset wise, I will randomly generate the 1D (or 2D) complex signals as the ground truths and the noise image as the training data.  
  > **username_1**: I would recommend that you [edit your post](https://ai.stackexchange.com/posts/34018/edit) include these details because comments are temporary and are only meant to ask for clarification. In addition, after editing, your question will pop up as an active post and people will maybe read it again.  
  > **username_1**: One thing that I think is still missing from your description in the post is how the image processing algorithm that converts 1d to 2d noise images works. If your 2d images are just noise and there's no pattern in them, I doubt you can do what you want. It may also be a good idea to provide the example of a 2d image that you have in mind and the corresponding 1d signal. Just to have a better idea of the types of inputs and outputs that you have.  
  > **username_0**: @nbro, yes thanks for the suggestions. I will edit accordingly.  
  > **username_2**: I am working on the same problem like 2d image to 1d signal. I also generated a 2d synthetic image from a 1d signal in plasma science and now try to reverse this. Is there any deep learning modal that can help with this? Please guild me.  
  > **username_0**: @DhruvilGorasiya Hi there. It has been quite a while. Eventually, I decided to make use of this repository in GitHub https://github.com/LLNL/macc. The research was also built upon a plasma physics problem. The main idea of the deep learning model is to recreate a 2D image from a set of parameters. To do this, a subnetwork is trained to take in a 2D image and then convert it into a 1D latent space before converting back in the 2D image. Although this may not be specifically what you need, I hope this can give you a direction.  
  
  ---
  
  ## 0 answers
  
  