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DCSCN

DCSCN is a Python and TensorFlow implementation of a deep convolutional neural network for single-image super-resolution. Its repository provides scripts to apply the model to image files, evaluate it on sample datasets and train with image data.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

DCSCN — OpenViST project profile in Image & Video AI.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use DCSCN when you can work with a local Python and TensorFlow project and need a script-based way to upscale images, evaluate a model or explore training on image data.

Who it is for

  • Developers who want to run or study a single-image super-resolution implementation.
  • Computer-vision practitioners considering an image-upscaling stage before downstream processing.
  • Researchers comparing or evaluating super-resolution models on sample datasets.

Documented uses

  • Apply super-resolution to an image file and save the result.
  • Evaluate the model on Set5, Set14 or BSD100 datasets.
  • Prepare a custom image dataset and configure it for model training.
Before you choose

Requirements & limits

  • The documented stack includes Python 3.5 or later and TensorFlow 2.0 or later, plus SciPy, NumPy, Pillow, ImageIO and scikit-image.
  • The repository includes some learned weights and pretrained models for evaluation.
  • The project advises matching inference arguments to those used during training.
  • The repository specifies the MIT license.

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

Apply DCSCN super-resolution to an image

Use the repository’s documented script to process an image file and produce super-resolution output.

Before you start

  • Python 3.5 or later
  • TensorFlow 2.0 or later, SciPy, NumPy, Pillow, ImageIO and scikit-image
  • An image file and the DCSCN project directory

Steps

  1. Place the image file in the project directory.

  2. Run the command below.

    Documented command
    python3 sr.py --file=your_file.png

  3. Use the output directory to find the generated result.

Expected outcome

The script generates a super-resolution result in the output directory.

Guide limitations

  • Use the same arguments as the model’s training configuration.
Documentation 1 ↗
Source-based practical guide

Prepare a custom image dataset for training

Organize image files and specify the dataset for DCSCN training.

Before you start

  • Python 3.5 or later
  • TensorFlow 2.0 or later, SciPy, NumPy, Pillow, ImageIO and scikit-image
  • Image files for a training dataset

Steps

  1. Put the training image files in a directory under data.

  2. Specify the dataset with the --dataset argument.

  3. Consult args.py for the other training parameters.

Expected outcome

The dataset is organized and the repository’s documented dataset and parameter configuration stages are complete.

Guide limitations

  • The README identifies training parameters as important and directs users to args.py; it does not give a complete command in the cited instructions.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
16 October 2026
Original website title
jiny2001/dcscn-super-resolution · Official homepage title · retained in its source language
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