Preserving the Past While Revitalizing It: AI and Photo Restoration

Most of us have boxes, albums, or envelopes of old photographs tucked away somewhere—family portraits, snapshots from vacations, candid moments that exist nowhere else. Over time, these images fade, scratch, discolor, or simply fail to live up to what we remember seeing with our own eyes. Sometimes the problem isn’t age at all, but the limitations of the camera, film, or lighting used at the moment the photo was taken.

The question this raises is simple but loaded: how do we responsibly breathe new life into these images without losing their authenticity?

The Problem: Revitalization Without Revisionism

Traditional photo restoration has always walked a careful line. Cleaning up dust, repairing tears, and correcting obvious color shifts are generally accepted as restoration. But at what point does restoration become alteration?

This question has become more contentious with the rise of AI-based tools—especially generative AI. For many people, AI in media creation triggers understandable skepticism: fears of fabrication, historical revisionism, or the subtle rewriting of visual truth. These concerns are not misplaced. AI is extremely good at inventing plausible details, and that power cuts both ways.

The goal of responsible photo restoration, however, is not to improve history, but to recover what was plausibly there and make it visible again.

A Brief Look at Traditional Approaches

Before AI, restoration was largely a manual craft. Tools like Photoshop gave restorers fine-grained control:

  • Healing and clone tools to remove dust and scratches
  • Manual color correction and tonal balancing
  • Layer-based workflows to preserve non-destructive edits

These methods are powerful and precise, but they are also time-consuming and require significant skill. For many people, the barrier to entry is simply too high. As a result, countless photos remain unrestored—not because they can’t be improved, but because doing so is impractical.

What AI Changes—and What It Shouldn’t

AI-based photo restoration tools offer something genuinely new: accessibility. Tasks that once took hours can now be done in seconds. Faded faces can be clarified, noise reduced, contrast restored, and even resolution increased enough for modern displays and prints.

But this convenience comes with responsibility.

Used carelessly, AI can:

  • Invent facial features that were never there
  • Over-sharpen skin and textures into something unnatural
  • Apply modern aesthetic biases to historical images

Used thoughtfully, AI can instead:

  • Enhance clarity without altering structure
  • Recover contrast and tonal range lost to aging or poor exposure
  • Improve legibility while keeping the original character intact

The difference is not the tool—it’s the intent and the process.

Principles for Responsible AI Photo Restoration

To use AI in a way that respects the original, a few guiding principles help keep things grounded:

  1. Start with the best possible scan AI cannot fix what isn’t captured. High-resolution, well-lit scans preserve maximum information and give both manual tools and AI better material to work with.

  2. Favor enhancement over invention Avoid tools or settings that add details rather than clarify existing ones. If an AI model is guessing, that should be a conscious choice—not an invisible default.

  3. Work incrementally Apply AI enhancements in stages and review each step. Subtle improvements compound; heavy-handed ones distort.

  4. Keep the original untouched Always retain the unmodified scan. Restoration should be reversible in principle, even if the tools themselves are not.

  5. Be transparent about what was done Especially when sharing restored images, it matters to say how they were produced. Transparency builds trust.

AI vs. Photoshop: A False Dichotomy

It’s tempting to frame this as a choice: AI or traditional tools. In practice, the best results often come from using both.

AI excels at:

  • Noise reduction
  • Global enhancement and upscaling
  • Recovering faces and textures degraded by time

Manual tools excel at:

  • Fine local corrections
  • Artistic judgment
  • Enforcing restraint where automation might overreach

In a thoughtful workflow, AI becomes an assistant—not an author.

Why This Matters

Photographs are more than images; they are personal and cultural records. When we restore them, we’re not just improving pixels—we’re shaping how the past is seen and remembered.

Used carefully, AI can help preserve those memories, making them accessible to future generations without rewriting their story. Used recklessly, it risks turning documentation into interpretation.

The challenge—and the opportunity—is to use these powerful new tools with humility, clarity of intent, and respect for the originals.

In the sections that follow, I’ll look at specific AI tools, practical workflows, and side-by-side examples that show where AI shines, where it falls short, and how to keep it working in service of the photograph rather than at its expense.

A Concrete Example: Restoring a Photo of My Father

To make this discussion less abstract, the remainder of this post walks through a real example from my own family photo collection: a photograph of my father taken decades ago. The original print is physically intact but shows the familiar signs of age—softness, tonal flattening, and subtle discoloration. It is also limited by the quality of the original camera and film.

I chose this image deliberately. Personal photographs raise the stakes. They are not just historical artifacts; they are emotionally loaded. Any restoration choices made here need to feel respectful, restrained, and honest.

Step 1: Digitization — Starting With a Faithful Capture

Rather than using a flatbed scanner, I digitized the photo using a DSLR camera setup:

  • Camera: Nikon D5100
  • Lens: Macro lens suitable for flat-field reproduction
  • Lighting: Even, diffuse lighting to avoid glare and texture exaggeration

A camera-based scan has a few advantages when done carefully: higher effective resolution, better control over focus and exposure, and the ability to capture subtle surface detail without the harshness some scanners introduce.

The goal at this stage was not to make the image look good—it was to make it accurate. Any errors introduced here would be amplified downstream.

Step 2: Minimal Cleanup in Lightroom

Before introducing any AI tools, I performed a restrained, conventional cleanup pass in Lightroom:

  • Corrected white balance to neutralize obvious color casts
  • Adjusted exposure and contrast conservatively
  • Applied very light dust and spot removal where defects were unambiguous
  • Avoided sharpening beyond what was needed to counter capture softness

This step is important philosophically as well as practically. It establishes a clean baseline and ensures that the AI tools are working from a reasonable representation of the photograph, not compensating for avoidable technical issues.

At this point, the image looked better, but still clearly showed its age and limitations. That’s exactly where I wanted to stop with manual tools.

Step 3: AI Restoration — Two Modern Tools

With a solid digital master in hand, I then turned to AI-based restoration. I used two different tools that, as of January 2026, represent the current state of the art for photographic restoration. I won’t name them here yet; the point of this section is the process and the outcomes, not brand promotion.

Both tools promise similar capabilities:

  • Facial detail recovery
  • Noise and artifact reduction
  • Perceptual sharpening
  • Resolution enhancement suitable for modern displays

What matters is how they deliver on those promises.

Comparing the Results

The differences between the two AI outputs were subtle but instructive.

  • One tool favored aggressive clarity, producing a striking result that initially felt impressive but risked smoothing and reinterpreting facial features.
  • The other tool took a more conservative approach, preserving texture and structure at the cost of slightly less visual punch.

Neither result was “right” or “wrong.” Each reflected a different set of assumptions baked into the model.

This is where human judgment becomes essential. I compared the AI outputs not just to each other, but back to the original scan and to my own memory of the subject. In several places, I deliberately rolled back or rejected AI changes that felt plausible but not faithful.

What I Chose—and Why

The final image I kept was not a raw AI output. It was a curated result: AI restoration followed by restraint. In some areas, I blended the AI-enhanced version with the original scan to keep skin texture and tonal transitions grounded.

This hybrid approach reinforces a central argument of this post: AI should assist restoration, not replace judgment.

What This Example Illustrates

This single photograph demonstrates several broader points:

  • High-quality digitization matters more than the choice of AI tool
  • Minimal manual cleanup before AI improves results and reduces overreach
  • Different AI tools embody different aesthetic and ethical assumptions
  • The restorer’s role does not disappear—it becomes more important

In the next section, I’ll show side-by-side crops from the original scan, the Lightroom-cleaned version, and both AI restorations, and I’ll call out specific areas where AI helped—and where it needed restraint.