DreamClear

DreamClear : High-capacity real-world image inpainting model

DreamClear is a deep learning model focused on inpainting and super-resolution processing of high-volume real-world images. It uses privacy and security data management technology to provide efficient solutions and was published at NeurIPS 2024.

Main advantages

High volume processing capability

Privacy protection

Efficiency

Pretrained models and code available

Available free of charge to research and industry

target users

Researchers developers in the field of image processing as well as industrial users who need to perform image super-resolution and inpainting. It is especially suitable for scenarios where large amounts of image data are processed and data privacy is important.

Application scenarios

Super-resolution processing of real-world blurred images

Image Clarification Processing in Surveillance Video Analysis

medical image enhancement

Product features

Image super-resolution

Privacy and security data management

Pretrained model

Support for various image processing tasks (e.g. segmentation detection)

Work with documentation and code

Ongoing updates and community support

Tutorial

1 Copy the DreamClear code base

2 Create a Conda environment and install the required Python packages

3 Download the pre-trained model Huggingface platform

4 Prepare training data high-resolution and low-resolution images

5 Use tools to generate pairing data

6 Training DreamClear model can adjust training parameters

7 Using trained models for super-resolution and inpainting

8 Evaluate model performance using the provided benchmarks