Can AI Restore Old Photos? How to Restore Old Photos? NadouPro Old Photo Restoration Workflow: From Scratch Removal to Super-Resolution Reconstruction

Old photo restoration is a unique application area in AI image processing. Unlike AI image generation, the core principle of restoration is "restore, not create"—removing damage accumulated over time and recovering the original information in the image, without changing the people, events, or objects in the photo. NadouPro's image editing capabilities provide a complete restoration workflow around this principle: Frame Interpolation / Ultra HD handles resolution and sharpness, image models handle damage and color, Relight corrects lighting issues, and face retouching performs local restoration.

What kinds of damage can AI restoration fix?

Damage in old photos is usually not a single issue, but a combination of multiple forms of degradation. A photo taken from an old album may have several problems at once: physical damage such as creases, scratches, and tear marks; color degradation such as yellowing, fading, and color casts; image quality loss such as blur, low resolution, and scanning noise; and lighting issues such as underexposure, excessive backlighting, and harsh shadows.

The key to AI restoration is distinguishing between "restoration" and "generation." Restoration removes noise from existing information—taking away scratches, dust, and color casts that cover the image so the original image underneath can appear. Generation creates content that does not exist in the image—AI guesses and fills in textures in missing areas. NadouPro's restoration workflow focuses mainly on restoration: Frame Interpolation / Ultra HD improves resolution based on existing pixels, Relight recalculates lighting based on the existing image, and face retouching optimizes details based on the existing facial structure, without inventing new facial features or changing a person's identity.

Damage type	Restoration method
Scratches, creases, tears	Restoration capability of image models, reconstructing damaged areas based on surrounding image content
Yellowing, fading, color casts	Intelligent color adjustment or image models to restore color balance
Blur, out of focus	Frame Interpolation / Ultra HD improves sharpness and resolution
Low resolution	Super-resolution enlargement with Frame Interpolation / Ultra HD
Underexposure or overexposure	Relight adjusts lighting parameters
Scanning noise and dust	Image models clean them automatically

Workflow: two paths for different restoration needs

Image restoration in NadouPro is not a one-click operation with a single tool. It is a workflow that selects different combinations of capabilities based on the type of damage.

Path 1: Frame Interpolation / Ultra HD—resolution and sharpness issues

When the main problem with a photo is low resolution or a blurry image, while the overall structure is intact and the color is normal, Frame Interpolation / Ultra HD is the fastest choice. Import the photo into the Image node in the canvas workflow, select the Frame Interpolation / Ultra HD capability, and set the enlargement factor. The system automatically completes super-resolution enlargement and detail sharpening—improving resolution while preserving the original texture of the image, without introducing blur or jagged edges due to enlargement.

This path is suitable for handling blurry photos taken with a phone, overly compressed online images, and film scans with insufficient scanning resolution. The operation is simple and does not require writing prompts. The system automatically selects the optimal parameters based on the quality of the source material.

Path 2: image models—comprehensive restoration for multiple types of damage

When a photo has physical damage, color degradation, and image quality issues at the same time, image models are needed for comprehensive restoration. NadouPro integrates multiple image models, and each model has different strengths across restoration dimensions:

• Seedream 5.0 Pro: Restoration quality at a cinematic image level, good at handling complex scenes and portraits, with restored image texture close to cinematic standards

• GPT Image 2: A hyper-realistic direction, suitable for restoring portrait photos that need to retain a realistic texture, and able to accurately preserve original facial details

• Nano Banana 2: Extremely fast image output, suitable for quick processing during batch restoration, with strong text rendering capability so text information in photos can be preserved well

When using image models for restoration, you need to write a restoration prompt in the Image node. The prompt describes the restoration goal and constraints, and its structure is relatively fixed:

Restore this old photo while preserving the identity characteristics, facial structure, expression, clothing, and historical authenticity of the people in the original photo. Remove scratches, dust, tear marks, stains, fading, discoloration, creases, mildew spots, noise, blur, and compression artifacts. Naturally reconstruct damaged areas based on surrounding image content, without creating new facial features or changing the person's appearance. Restore details in the eyes, skin, hair, clothing, and background. Correct exposure, contrast, and white balance. Improve clarity, preserve real texture, and avoid overprocessing or a plastic look.

The core logic of this prompt is four layers: "preserve + remove + restore + correct." Preserve identity characteristics, remove damage, restore details, and correct parameters. The model performs restoration based on this framework and does not deviate from the principle of "restoration."

Fine adjustments: Relight and local restoration

After the overall restoration is complete, if the photo still has specific lighting problems or local flaws, more precise tools are needed for targeted adjustments.

Relight to correct lighting

Lighting problems are very common in old photos: overly harsh shadows caused by indoor flash, underexposure caused by backlit shooting, and color temperature shifts at different times in daylight. Relight can recalculate lighting on the restored image and adjust it across three dimensions: direction, color temperature, and brightness.

How it works: select the Image node that needs adjustment on the canvas and enable Relight. Set the light source direction, such as quick positions for top, front, left, right, back, or manually drag for fine adjustment; choose soft light or hard light mode; and adjust brightness. The system recalculates the lighting distribution across all surfaces in the image—the person's face, clothing folds, and background objects—while keeping the overall image consistent.

Relight is especially useful when restoring family group photos. Built-in flashes on old cameras often caused overexposed faces and overly dark backgrounds when shooting from the front. Relight can rebalance the brightness of the image and restore a natural sense of depth.

Face retouching for local restoration

When damage in a photo is concentrated in a specific area—a scratch on a person's face, a stain somewhere, or an unwanted object in the background—the instruction editing capability of face retouching can make local changes without affecting the rest of the image.

By describing the area that needs to be changed and how it should be changed, the system adjusts only the specified range. For example, "remove the tear mark in the lower-right corner of the photo," "restore the faded area around the person's left eye," or "remove the utility pole in the background." Each instruction changes only the specified content, while the rest of the image remains completely untouched. This is safer than full-image restoration—when most of a photo is in good condition and only a few areas need processing, full-image restoration may instead change parts that do not need modification.

Restoration use cases in film and video projects

The value of AI photo restoration in film and video creation is not limited to preserving personal memories. It also has practical project-level applications:

Historical material processing. When producing period dramas or historical documentaries, historical photos often need to be restored as reference materials or used directly in visuals. These photos are usually heavily damaged and require multiple rounds of restoration. For batch processing, first use Frame Interpolation / Ultra HD to standardize and improve resolution, then use image models to restore damage one by one, and finally use Relight to unify the lighting style.

Character reference image optimization. When using real-person photos as reference inputs for character design, photo quality directly affects the generated results. Restoring blurry or low-quality reference photos and improving facial clarity and color accuracy can make subsequent character generation more stable.

IP material management. Historical materials and brand archive photos used in series content can be reused across multiple projects after restoration, improving the efficiency of material usage.

Frequently asked questions

Q: What enlargement factors does Frame Interpolation / Ultra HD support?

Frame Interpolation / Ultra HD performs automatic super-resolution and selects the optimal parameters based on the quality of the original material and the target output needs. Smaller source photos can use higher magnification, while large photos only need lower magnification.

Q: Can I restore only one area of a photo?

Yes. Use the instruction editing feature of face retouching to describe the area and content that need to be modified. The system will adjust only the specified range without affecting other parts.

Q: If the main problem with a photo is poor lighting, which tool should I use?

Relight can directly adjust light source direction, color temperature, and brightness. It is suitable for lighting issues such as underexposure, overly harsh flash shadows, and color temperature shifts.

Q: Do I need to write complex prompts for restoration?

No. The prompt structure is relatively fixed. Simply describe the types of damage that need to be repaired and the features that need to be preserved. The "preserve + remove + restore + correct" framework above applies to most restoration scenarios.

Q: Can I restore multiple photos in batches?

Yes. Frame Interpolation / Ultra HD supports one-click processing without prompts, making it suitable for uniformly improving resolution in batches. Image model restoration can be performed one photo at a time. It is recommended to test the effect on representative photos first, then apply the parameters in batches after confirmation.

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