SRFBN_CVPR19

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SRFBN_CVPR19 is an Open Source Content & Design tool that provides PyTorch code for image super-resolution. It implements a feedback network to enhance image quality and detail.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is SRFBN_CVPR19?

SRFBN_CVPR19 offers a PyTorch implementation of the 'Feedback Network for Image Super-Resolution' paper (CVPR2019). This open-source tool allows users to apply advanced deep learning techniques to enhance the resolution of images. It includes pre-trained models for various scaling factors and degradation models, enabling quick evaluation on standard benchmarks like Set5, Set14, and Urban100. Users can also train their own models using provided scripts and datasets like DIV2K or DF2K. The repository details the architecture of the SRFBN, including feedback connections, and provides comprehensive instructions for testing and training, making it a valuable resource for researchers and developers in computer vision.

Best used for

Ideal for researchers and developers who need to implement and experiment with state-of-the-art image super-resolution techniques. Especially valuable for those working on computer vision projects requiring high-quality image reconstruction and model development.

Common actions

enhance image resolution

implement super-resolution models

train image enhancement models

evaluate image quality

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Capabilities

Key features

PyTorch code
Image super-resolution
Feedback network architecture
Pre-trained models
Custom model training
Benchmark evaluation

Target Audience

researchersdeveloperscomputer vision engineersstudents

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What programming languages and frameworks are required to use SRFBN_CVPR19?

SRFBN_CVPR19 is primarily developed with Python 3, utilizing the PyTorch deep learning framework. It also requires libraries such as skimage, imageio, tqdm, pandas, and cv2. Matlab is used for preparing training and test data, as well as for evaluating results.

Can I use SRFBN_CVPR19 to enhance my own images?

Yes, you can test SRFBN_CVPR19 on your own images. After cloning the repository and downloading the pre-trained models, you can place your images in the specified directory and run the provided test script to generate super-resolved outputs.

Does SRFBN_CVPR19 support different image degradation models?

Yes, SRFBN_CVPR19 supports various image degradation models, including Bicubic (BI), Bicubic Downsampling (BD), and Gaussian Noise (DN). You can configure these models when preparing your training and test data using the provided Matlab scripts.

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Coding & Development › Open Source & Models

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