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Ultra-High-Resolution Portrait Restoration

STRUCTURED
Prompt2890 chars · 335 words
{
  "prompt": "Restore and fully enhance this old, blurry, faded, and damaged portrait photograph. Transform it into an ultra-high-resolution, photorealistic image with HDR-like lighting, natural depth-of-field, professional digital studio light effects, and realistic bokeh. Apply super-resolution enhancement to recreate lost details in low-resolution or blurred areas. Smooth skin and textures while preserving all micro-details such as individual hair strands, eyelashes, pores, facial features, and fabric threads. Remove noise, scratches, dust, and artifacts completely. Correct colors naturally with accurate contrast and brightness. Maintain realistic shadows, reflections, and lighting dynamics, emphasizing the subject while keeping the background softly blurred. Ensure every element, including clothing and background textures, is ultra-detailed and lifelike. If black-and-white, restore accurate grayscale tones with proper contrast. Avoid over-processing or artificial look. Output should be a professional, modern, ultra-high-quality, photorealistic studio-style portrait, preserving authenticity, proportions, and mood, completely smooth yet ultra-detailed.",
  "steps": [
    {
      "step": 1,
      "action": "Super-resolution",
      "description": "Upscale the image to ultra-high-resolution (8K or higher) to recreate lost details."
    },
    {
      "step": 2,
      "action": "Deblur and repair",
      "description": "Fix blur, motion artifacts, scratches, dust, and other damage in the photo."
    },
    {
      "step": 3,
      "action": "Texture and micro-detail enhancement",
      "description": "Smooth skin and surfaces while preserving ultra-micro-details such as pores, hair strands, eyelashes, and fabric threads."
    },
    {
      "step": 4,
      "action": "Color correction",
      "description": "Adjust colors naturally, maintain realistic contrast and brightness, simulate modern camera color science."
    },
    {
      "step": 5,
      "action": "HDR lighting and digital studio effect",
      "description": "Apply HDR-like lighting, professional digital studio lighting, realistic shadows, reflections, and controlled depth-of-field with soft bokeh background."
    },
    {
      "step": 6,
      "action": "Background and detail restoration",
      "description": "Ensure background elements, clothing, and textures are sharp, ultra-detailed, and clean, while preserving natural blur for depth."
    },
    {
      "step": 7,
      "action": "Grayscale adjustment (if applicable)",
      "description": "Restore black-and-white portraits with accurate grayscale tones and proper contrast."
    },
    {
      "step": 8,
      "action": "Final polishing",
      "description": "Avoid over-processing, maintain a natural and authentic look, preserve original mood and proportions, ensure ultra-smooth yet ultra-detailed output."
    }
  ]
}

Overview

Ultra-High-Resolution Portrait Restoration is a free prompt on OpenRuna. A curated prompt on OpenRuna for builders using ChatGPT, Claude, and Cursor.

What this prompt does

"Ultra-High-Resolution Portrait Restoration" is designed to help you get reliable results from AI assistants for real prompt tasks. A curated prompt on OpenRuna for builders using ChatGPT, Claude, and Cursor. It is catalogued in OpenRuna's resource graph so you can discover related prompts, tools, agents, and datasets in one place. Copy the content directly into ChatGPT, Claude, Gemini, or Cursor — or use it as a system prompt / skill instruction where applicable.

Use cases

  • Use "Ultra-High-Resolution Portrait Restoration" when you need a repeatable prompt for professional workflows without writing instructions from scratch each time.
  • Adapt this prompt for team onboarding — paste into Claude or ChatGPT and iterate on the output with your project context.
  • Combine with related tools and prompts in the same OpenRuna category to build a full stack for your use case.
  • Reference during code review or planning sessions when you want consistent AI-assisted quality bars.

Example output

When you run this prompt, expect structured output similar to:

{
  "prompt": "Restore and fully enhance this old, blurry, faded, and damaged portrait photograph. Transform it into an ultra-high-resolution, photorealistic image with HDR-like lighting, natural depth-of-field, professional digital studio light effects, and realistic bokeh. Apply super-resolution enhancement to recreate lost details in low-resolution or blurred areas. Smooth skin and textures while preserving all micro-details such as individual hair strands, eyelashes, pores, facial features, and fabric threads. Remove noise, scratches, dust, and artifacts completely. Correct colors naturall…

Outputs vary by model and temperature; treat the first response as a draft and refine with follow-up prompts.

Tips by platform

Claude

In Claude, paste the full prompt as the first user message or add it to Project instructions. Ask Claude to confirm assumptions before executing. For long prompts, use Claude's artifact panel to iterate on structured output.

ChatGPT

In ChatGPT, start a new chat and paste this prompt verbatim. Enable GPT-4o or your preferred model for coding tasks. Use follow-ups like "apply this to [your context]" for best results.

Cursor

In Cursor, add key instructions from this prompt to .cursorrules or a SKILL.md file. Reference it in Agent mode with @ mentions. Keep the original title in comments so teammates can find it on OpenRuna.

Frequently asked questions

What is "Ultra-High-Resolution Portrait Restoration"?
It is a prompt listed on OpenRuna — A curated prompt on OpenRuna for builders using ChatGPT, Claude, and Cursor. You can copy and adapt it for ChatGPT, Claude, Cursor, or other AI tools.
Is this prompt free to use?
Most OpenRuna resources are open or CC0-licensed. Check the license on this page before commercial use. Premium collections are clearly marked.
How do I get the best results?
Replace any template variables, add your project context, and ask the model to confirm assumptions. Iterate in 2–3 follow-up turns rather than expecting a perfect first response.
Can I use this with Claude and ChatGPT?
Yes. The prompt is model-agnostic text. Tips on this page cover Claude, ChatGPT, and Cursor specifically.
Where can I find related resources?
Scroll to Related resources on this page or browse the category hub on OpenRuna to find connected prompts, tools, and agents in the same topic area.

Related resources