AI Summary

Overview

Jasna is a powerful, GPU-accelerated tool designed for advanced video and image restoration, specifically targeting the enhancement of pixelated or "mosaic" artifacts. It offers both a user-friendly application and a versatile command-line interface, making it accessible to a wide range of users, from hobbyists to professional content creators. Jasna intelligently isolates and restores only the affected areas, preserving the integrity of the rest of your footage.

Key Features

  • Targeted Mosaic Restoration: Precisely identifies and enhances pixelated regions within videos and images, leaving untouched areas unaltered.
  • Dual Restoration Models: Choose between the fast, free BasicVSR++ model for quick results or the more detailed, but slower, LTX supporter model (NVIDIA only) for superior quality.
  • Interactive Segment Editor: Precisely select and preview areas for restoration, giving you granular control over the enhancement process.
  • Live Video Playback: Preview restored video in real-time without generating temporary files.
  • VR180 and Still Image Support: Works with immersive VR180 content and can restore still images (SD 1.5, supporter feature).
  • GPU Acceleration: Leverages your NVIDIA or AMD GPU for significantly faster processing times.
  • Command-Line Interface: Automate restoration tasks and integrate Jasna into existing workflows.

Typical Use Cases

  • Enhancing Archived Footage: Revitalize old videos that have suffered from compression artifacts or digital noise.
  • Improving Low-Quality Video Sources: Upscale and sharpen modern video content that was recorded with lower quality settings or compressed heavily.
  • Restoring Images with Digital Artifacts: Clean up pixelated or blocky areas in still photographs.
  • Content Creator Workflow: Quickly preview and apply targeted restorations to video projects, saving time and resources.
  • VR Content Restoration: Enhance the visual fidelity of immersive VR180 videos.

Repository Trajectory

Trended #9 on daily

October 20, 2026

Trended #12 on daily

October 19, 2026

First tracked on GitTrend

October 19, 2026

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Contributors

Languages

C++

1%

Cuda

7%

Python

92%