How to Super-Resolve Images Using SRCNN with TensorFlow

Jan 25, 2021 | Data Science

Super-resolution is a fascinating area in the field of image processing, allowing you to enhance the quality of low-resolution images. In this blog, we will walk you through the process of implementing Super-Resolution Convolutional Neural Network (SRCNN) using TensorFlow. With the right tools and this guide, you’ll be able to recreate super-resolved images similar to those presented in the original paper.

Prerequisites

Before diving into the implementation, make sure you have the following prerequisites in place:

  • TensorFlow
  • Scipy version 0.18 (ensure mode option from scipy.misc.imread function is available)
  • h5py
  • matplotlib

The choice of libraries is crucial here. It’s worth noting that instead of using Matlab or OpenCV—which can be complicated to install, especially on Linux—we are utilizing Scipy. This makes the process smoother and more user-friendly. For more information about Scipy, click here.

Usage Instructions

Now that we have the setup, here’s how you can execute the SRCNN code:

  • To train the model, simply run:
  • python main.py
  • For testing, you can run the following command:
  • python main.py --is_train False --stride 21

Understanding the Code: An Analogy

Imagine you’re an artist trying to recreate a masterpiece from a low-resolution image that looks like a pixelated mess. The SRCNN acts like a skilled assistant who has a rich understanding of art techniques. When you feed the low-resolution image (the canvas), the SRCNN analyzes the patterns, features, and textures, applying its extensive training to fill in the details and recreate a sharper, more detailed high-resolution image (the masterpiece). This analogy serves to explain how a convolutional neural network systematically processes images to enhance them using learned features.

Results

After training for an impressive 15,000 epochs, I managed to achieve super-resolved images that closely resemble those found in the reference paper. Here’s a summary of my experience:

Training Time: 12 hours, 16 minutes, and 1.41 seconds.

Desktop Performance:

  • Intel I7-6700 CPU
  • GTX970
  • 16GB RAM

Below are the comparison images from the results of the SRCNN implementation:

Original Butterfly Image:

Original Butterfly Image

Bicubic Interpolated Image:

Bicubic Interpolated Image

Super-Resolved Image:

Super-Resolved Image

Troubleshooting Ideas

If you encounter any issues during the installation or execution of the SRCNN model, here are some troubleshooting steps you can follow:

  • Verify that all necessary libraries are installed and at the correct versions.
  • Check for compatibility issues with your GPU if TensorFlow fails to recognize it.
  • Ensure that you are running the correct command for either training or testing.
  • If you experience long training times, consider reducing the number of epochs initially and gradually increasing them as you optimize the process.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.

References

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