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Srcnn Tensorflow

Srcnn Tensorflow is a Python and TensorFlow implementation of a convolutional neural network for image super-resolution, with documented commands for training and testing images.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Srcnn Tensorflow — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when you want a code-based SRCNN starting point for training or testing image super-resolution, rather than a hosted image-enhancement service.

Who it is for

  • Developers studying or adapting an SRCNN implementation.
  • Researchers experimenting with image super-resolution and reconstruction.

Documented uses

  • Train the SRCNN model using the repository's main script.
  • Run its documented testing mode on images.
  • Inspect example comparisons of original, bicubic-interpolated and super-resolved imagery.
Before you choose

Requirements & limits

The repository is public and lists an MIT license.

  • The README lists TensorFlow, SciPy above 0.18, h5py and matplotlib as prerequisites.
  • The project uses SciPy instead of Matlab or OpenCV for its image-handling approach.
  • The README gives separate commands for training and testing but does not detail input preparation.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Run the documented training or testing command

Use the repository's README commands to start model training or run its stated testing mode.

Before you start

  • Python
  • TensorFlow
  • SciPy version above 0.18
  • h5py
  • matplotlib

Steps

  1. Obtain the Srcnn Tensorflow repository.

  2. Install the listed prerequisites.

  3. For training, run the command below.

    Documented command
    python main.py

  4. For testing, run the command below.

    Documented command
    python main.py --is_train False --stride 21

Expected outcome

The documented command starts the training workflow or invokes the testing mode with stride 21.

Guide limitations

  • The README does not detail input-image preparation or further testing options.
  • The author reports one training example of 15,000 epochs taking about 12 hours and 16 minutes on an Intel i7-6700, GTX 970 and 16 GB RAM; this is not a general runtime estimate.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
16 October 2026
Original website title
tegg89/SRCNN-Tensorflow · Official homepage title · retained in its source language
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