Image Super Res 1.4.0

Image Super Res 1.4.0

Nobutaka Yuasa  ❘ Freeware
iOS
out of 3 votes
Latest Version
1.4.0
Safe to install

The application employs artificial intelligence techniques to upscale and enhance image quality effectively.

It accommodates a diverse range of image formats, including photographs, anime, and illustrations, offering scaling options of x2 and x4. Users can improve the resolution and visual clarity of their images through a straightforward, user-friendly interface.

To utilize the app, simply select images from the Photo Library or Files, then tap the Upcale button—it's an intuitive process. Experimenting with different AI models allows users to observe varying enhancement effects.

Supported Image Formats:

  • GIF
  • HEIC
  • JPEG
  • PNG
  • WebP

Supported AI Models:

  • RealESRGAN x4 Plus: Designed for general images with a scale factor of x4
  • RealESRGAN x2 Plus: Designed for general images with a scale factor of x2
  • RealESRNet x4 Plus: Another general-purpose model at x4 scale
  • realesr general x4v3: A compact version optimized for x4 scaling
  • RealESRGAN x4 Plus Anime: Tailored specifically for anime and illustration images at x4 scale

Recognition is given to Wang, X., Xie, L., Dong, C., & Shan, Y. (2021), whose work "Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data" informed the development of these models, presented at ICCVW.

Further technical details and updates are available on the project's GitHub page: https://github.com/xinntao/Real-ESRGAN.

Overview

Image Super Res is a Freeware software in the category Audio & Multimedia developed by Nobutaka Yuasa.

The latest version of Image Super Res is 1.4.0, released on 05/28/2025. It was initially added to our database on 05/28/2025.

Image Super Res runs on the following operating systems: iOS.

Users of Image Super Res gave it a rating of 3 out of 5 stars.

Pros

  • Utilizes advanced super-resolution algorithms to enhance low-resolution images.
  • Capable of restoring fine details and improving image clarity.
  • Potential to be integrated into various imaging workflows for better results.
  • Designed for research and experimental purposes, allowing for development and customization.

Cons

  • Limited publicly available information on the software's features and user interface.
  • May require technical expertise to operate effectively.
  • Performance and quality depend on the specific algorithms implemented which are not detailed publicly.
  • Potential for high computational resource requirements depending on image size and processing complexity.

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