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Dped

Dped is a Python deep-learning project that provides code and pretrained models to enhance ordinary smartphone photos toward DSLR-quality images. Its repository includes scripts for training camera-category models and applying pretrained models to test images, with documented resolution choices including original image resolution.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Dped — 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 Dped when you want code and pretrained models for a locally run deep-learning photo-enhancement workflow, or when you want to train a model using the project’s dataset and VGG-19 setup.

Who it is for

  • Developers building a local photo-enhancement workflow from Python code and pretrained models.
  • Researchers or practitioners who want to train image-enhancement models using the DPED dataset.
  • Users who need to process test images at selected resolutions with camera-category models.

Documented uses

  • Enhance smartphone photos toward DSLR-style image quality.
  • Apply provided pretrained models to test images.
  • Train an enhancement model for the documented iPhone, Blackberry or Sony categories.
  • Process images at original or selected test resolutions.
Before you choose

Requirements & limits

  • This is a code repository rather than a hosted editing service; the documented workflows use local scripts.
  • Testing and training have distinct requirements: the README lists the DPED dataset for testing, while training additionally uses a pretrained VGG-19 model and dataset folders.
  • The stated software stack includes Python image libraries, TensorFlow 1.x or 2.x, CUDA and cuDNN, with an NVIDIA GPU listed as a prerequisite.
  • Testing exposes a use_gpu option and resolution settings, including orig; the README does not establish performance on particular hardware.
  • The README warns that smaller training batches may cause unstable training.

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

Test images with a provided pretrained model

Apply one of Dped’s provided pretrained camera models to test images.

Before you start

  • Python with Pillow, scipy, numpy and imageio
  • TensorFlow 1.x or 2.x with CUDA and cuDNN
  • NVIDIA GPU is listed among the prerequisites
  • The DPED dataset in the dped/ folder

Steps

  1. Place the DPED dataset in dped/.

  2. Run the documented test script with a provided model, such as the command below.

    Documented command
    python test_model.py model=iphone_orig

  3. Set documented options such as test_subset, resolution and use_gpu as needed.

Expected outcome

Processed test images are saved as visual results; the repository lists visual_results/ for enhanced test images.

Guide limitations

  • The README specifies provided model choices as iphone_orig, blackberry_orig and sony_orig.
  • The dataset and software prerequisites must be prepared locally.
  • The README notes GPU memory errors can occur; it does not provide a complete machine-specific compatibility guarantee.
Documentation 1 ↗
Source-based practical guide

Prepare data and dependencies for model training

Set up the documented dataset and VGG-19 prerequisites for Dped training.

Before you start

  • Python with Pillow, scipy, numpy and imageio
  • TensorFlow 1.x or 2.x with CUDA and cuDNN
  • NVIDIA GPU
  • DPED training dataset and pretrained VGG-19 model

Steps

  1. Download the pretrained VGG-19 model and place it in vgg_pretrained/.

  2. Download the DPED dataset and extract it into dped/.

  3. Confirm that dped/ contains the sony/, iphone/ and blackberry/ subfolders.

Expected outcome

The documented data and model prerequisites are arranged for the repository’s subsequent training stage.

Guide limitations

  • This guide covers setup only; training itself requires a camera model and the documented training command.
  • The README warns smaller training batches can lead to unstable training.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
17 October 2026
Original website title
aiff22/DPED · Official homepage title · retained in its source language
How we review sourcesSuggest a correction

Related options

PyNET — A separate project linked in the Dped README for enhancing RAW photos.

PyNET-Bokeh — A separate project linked in the Dped README for rendering a bokeh effect.