AI video remastering: how it works and what results to expect

1. What is AI remastering?

AI remastering consists of applying deep learning models to a digitised video recording to improve its quality automatically and intelligently. The process starts from the digital file captured from the original tape and subjects it to several stages of neural processing.

Unlike a simple sharpening filter or automatic colour corrector, AI analyses each frame in context: it identifies faces, textures, edges, and patterns, and reconstructs visual information that the original analogue format could not record. The result is not an invention: it is a statistical reconstruction based on what the neural network has learned about what high-resolution images look like.

Advances in hardware (latest-generation GPUs) and neural network architectures (transformers, diffusion) have made it possible for this processing to be performed in reasonable times and with results that five years ago would have seemed like science fiction.

2. How it works: the stages of the process

The AI remastering workflow consists of several stages, each executed by specialised models:

Stage 1: Stabilisation

Analogue recordings exhibit unwanted movement: camera shake, player mechanism vibrations, and tracking fluctuations. An AI model analyses the movement between consecutive frames and applies corrections that stabilise the image without excessive cropping.

Stage 2: Noise removal (denoising)

Electronic noise is the most visible artefact in analogue videos: grain, random colour spots, horizontal bands. Denoising models distinguish between noise and real detail, removing the former without destroying the latter. This is the stage with the greatest visual impact.

Stage 3: Upscaling (super-resolution)

VHS has an effective resolution of approximately 240 horizontal lines (equivalent to less than 320x240 pixels). Super-resolution models generate coherent additional pixels to scale the image to 1080p, 2K, or even 4K. It is not a simple stretch: the AI reconstructs edges, textures, and facial details convincingly.

Stage 4: Colour correction

The colours of analogue tapes degrade over time: colour casts, yellowish whites, washed-out blacks. The AI analyses the chromatic distribution and applies corrections that restore natural tones whilst maintaining the aesthetic of the era.

Stage 5: Audio enhancement

Audio processing models remove background hiss, reduce electrical hum, and improve voice intelligibility. Some advanced systems can even separate tracks (voice, music, ambient noise) and rebalance them.

3. Real results: what to expect

Results vary depending on the quality of the original material and the format. These are the most common scenarios:

• •VHS in good condition: spectacular improvement. Noise virtually disappears, faces gain definition, colours become natural. The difference between the original and the remastered version is immediately visible.
• •Deteriorated VHS: notable improvement but with limitations. AI can reduce artefacts and improve sharpness, but cannot recover information that has been completely lost (dropout areas, tracking bars).
• •Hi8 / S-VHS: having a higher starting resolution, AI upscaling produces even more impressive results. Remastered Hi8 recordings can look as though they were filmed with modern equipment.
• •Betacam SP: the highest-quality analogue professional format benefits enormously from upscaling. The result can reach current broadcast quality.

4. Limitations of the technology

It is important to have realistic expectations. AI remastering is not magic, although it can sometimes seem like it:

• •It does not create non-existent information: if an area of the frame is completely destroyed (dropout), the AI can fill it with an approximation, but not with the real content.
• •Very distant or blurry faces: facial reconstruction works well with medium-sized faces, but if the face occupies very few pixels in the original, the result will be a generic approximation.
• •Fast movement: scenes with heavy movement may present temporal artefacts (ghosting) in some AI models.
• •It does not replace a good capture: remastering works on the digital file. If the original capture is poor (without TBC, with excessive compression), the AI has less to work with.

5. AI vs. traditional processing

Traditional video processing uses deterministic algorithms: sharpening filters (unsharp mask), threshold-based noise reduction, bicubic or Lanczos upscaling. These methods are efficient but limited:

6. When is remastering worthwhile?

AI remastering is especially recommended in these cases:

• •Family recordings of unrepeatable moments (weddings, births, trips) that you want to enjoy on modern screens.
• •Professional or institutional material to be reused in current productions.
• •Historical audiovisual archives intended for long-term preservation.
• •Any recording whose content has significant emotional, historical, or documentary value.

Videoconversion Digital Lab was the first audiovisual digitisation company to offer AI remastering as an accessible service for individuals and institutions. With technology developed by its own team in Barcelona and over +420,000 tapes processed in 22 years, the remastering service costs an additional 50% on top of the base digitisation price. It can be requested alongside digitisation or applied later to already digitised files.