What Is NAFNet? The Denoising and Deblurring Model Behind Modern Photo Restoration

Behind every AI photo restoration pipeline that handles grain, noise, and blur, there is typically a denoising or deblurring model operating before the more visible upscaling and face enhancement steps. NAFNet — Nonlinear Activation Free Network — is one of the most effective models for these tasks, and its design is notable for achieving state-of-the-art performance through a counterintuitive simplification: removing the complex activation functions that most neural networks rely on.

Understanding what NAFNet does, how it achieves its performance, and where it fits in the restoration pipeline gives you insight into a critical but often-overlooked step in AI photo restoration.

What Is NAFNet and Why Was It Developed?

NAFNet (Nonlinear Activation Free Network) was developed by researchers at Megvii Technology and published in a 2022 paper: "Simple Baselines for Image Restoration." The paper's central argument was provocative: that many of the architectural complexities added to image restoration networks over the years — nonlinear activation functions, complex attention mechanisms, elaborate normalization layers — were not actually necessary for achieving state-of-the-art performance.

By carefully simplifying the network architecture and removing nonlinear activation functions like GELU, ReLU, and Softmax from the core computation blocks, the researchers showed that a simpler, cleaner architecture could outperform much more complex models on standard benchmarks for denoising, deblurring, and general image restoration.

The name "Nonlinear Activation Free" reflects this core simplification: the standard nonlinear activation functions used in most deep learning architectures are replaced with simpler, more efficient operations.

How Does NAFNet Achieve State-of-the-Art Performance Without Complex Activations?

The key architectural innovation in NAFNet is the NAFBlock — a building block that replaces standard activation functions with simpler alternatives:

Simple Gate: Instead of using GELU (Gaussian Error Linear Unit) activation, NAFNet uses a "simple gate" operation — element-wise multiplication of two linear projections of the input. This is computationally simpler than GELU but achieves similar gating behavior, allowing the network to selectively emphasize relevant features.

Simplified Channel Attention: Standard channel attention mechanisms use Softmax over channel features, which involves an exponential computation. NAFNet replaces this with a simpler sigmoid-based operation, achieving similar attention behavior with less computational overhead.

Layer Normalization: NAFNet uses layer normalization rather than batch normalization, which improves training stability for image restoration tasks where batch sizes are often small due to memory constraints.

The result is a network that is faster to train, faster to run at inference time, requires less memory, and achieves competitive or superior performance compared to architectures with far more complex components. The "Simple Baselines" paper demonstrated that NAFNet achieved state-of-the-art PSNR (Peak Signal-to-Noise Ratio) on the SIDD denoising benchmark and the GoPro deblurring benchmark, surpassing previous methods that used significantly more complex architectures.

What Benchmark Datasets Does NAFNet Train and Evaluate On?

NAFNet's denoising capability is evaluated primarily on the SIDD dataset (Smartphone Image Denoising Dataset). SIDD contains approximately 30,000 real noisy and clean image pairs captured on real smartphone cameras in real indoor and outdoor environments. Unlike synthetic noise benchmarks that add Gaussian noise to clean images, SIDD contains actual sensor noise from real cameras — making models trained on it substantially more effective on real photographic noise than models trained on synthetic noise.

For deblurring, NAFNet evaluates on the GoPro dataset, which contains real motion blur sequences. The GoPro dataset was created by filming with a high-speed camera at 240 fps, then averaging adjacent frames to create realistic motion blur, with the original high-speed frames serving as the sharp reference. This produces genuine motion blur — not synthetic Gaussian blur applied to static images — making it a robust benchmark for real-world camera shake and motion blur.

NAFNet's strong performance on both of these realistic benchmarks is the main reason it has been adopted as a component in production photo restoration pipelines.

How Does NAFNet Fit in the Photo Restoration Pipeline?

NAFNet typically operates as an early step in the restoration pipeline, before upscaling and face enhancement. This ordering is deliberate and important.

Denoising before upscaling: Running Real-ESRGAN on a noisy image causes the upscaler to amplify noise texture alongside genuine detail. By running NAFNet denoising first, the input to the upscaler is cleaner, and the upscaler can focus on synthesizing genuine high-frequency detail rather than amplifying grain. The quality improvement from this ordering is clearly visible in side-by-side comparisons.

Deblurring before face enhancement: Blur in a face region reduces the amount of identity information available to GFPGAN's face prior alignment. Running NAFNet deblurring before GFPGAN face restoration gives the face restoration model a sharper starting point, allowing it to better preserve the specific person's facial geometry rather than filling in with generic face-prior content.

The denoising and deblurring steps add a relatively modest amount of processing time (5 to 15 seconds each) while significantly improving the quality of every subsequent step in the pipeline.

What Types of Photo Damage Does NAFNet Handle?

NAFNet is best suited for two primary damage categories:

Film grain and sensor noise: The characteristic granular texture of film photographs (silver grain) and the random pixel variation of digital photographs taken at high ISO settings. NAFNet trained on SIDD handles both types effectively because real-world sensor noise shares statistical properties across camera types.

Camera shake and motion blur: The directional smearing of image content caused by camera movement during a long exposure or subject movement during capture. NAFNet trained on the GoPro dataset handles a range of blur types, magnitudes, and directions.

NAFNet is less suited for: structured periodic noise (such as moire patterns from scanning printed halftone images), large physical tears or missing sections (which require inpainting rather than denoising), or color shifts and fading (which require different approaches entirely). For comprehensive old photo restoration, NAFNet is one component in a pipeline that addresses these additional damage types with specialized models.

How Does NAFNet Compare to Other Denoising Methods?

Classic methods like BM3D (Block-Matching and 3D Filtering) are sophisticated hand-crafted algorithms that use non-local similarity and 3D filtering in the transform domain. They are effective on Gaussian noise but less effective on real sensor noise because they are designed for specific noise statistics. They require no training data and have no GPU requirement.

Earlier deep learning methods like DnCNN and FFDNet trained CNNs for denoising and substantially outperformed classic methods on both synthetic and real noise. However, they required separate networks for different noise levels or noise types.

NAFNet represents the current state of the art in the efficient deep learning denoising category — it handles a wide range of noise types and levels with a single model, runs efficiently on GPU hardware, and achieves state-of-the-art benchmark scores. The simplicity of its architecture is also an advantage for deployment: fewer components means fewer potential failure modes and more predictable behavior on inputs outside the training distribution.

Frequently Asked Questions

What does "nonlinear activation free" mean in NAFNet's name?

In standard deep learning architectures, nonlinear activation functions are applied after linear operations (matrix multiplications) to introduce nonlinearity into the network — without which deep networks would collapse to a single linear transformation regardless of depth. Common activation functions include ReLU (Rectified Linear Unit), GELU (Gaussian Error Linear Unit), and Sigmoid. NAFNet's researchers found that by replacing standard activation functions with simpler alternatives — specifically, element-wise multiplication as a gating mechanism — the network retained its ability to learn complex nonlinear mappings while becoming computationally simpler. The "nonlinear activation free" name means the standard activation functions have been removed from the core building blocks, not that the network is fully linear. The element-wise gating operation that replaces them introduces nonlinearity in a simpler, more efficient form.

How does NAFNet perform on film grain specifically, compared to digital sensor noise?

Film grain and digital sensor noise have different statistical properties. Film grain arises from the random arrangement of silver halide crystals in photographic emulsion — it produces spatial patterns that correlate with scene content (grain is finer in bright areas, coarser in shadow areas on many film stocks). Digital sensor noise arises from photon shot noise, read noise, and thermal noise — it is more spatially uniform but can have different color channel statistics. NAFNet is trained on the SIDD dataset, which contains real smartphone sensor noise. It handles digital noise extremely well. For film grain, the statistical properties are similar enough to real sensor noise that NAFNet generalizes effectively. The model distinguishes grain from fine subject detail (rather than smoothing both uniformly) because it has learned the actual statistical signature of noise versus genuine texture from thousands of real noise examples. Results on both film and digital noise types are excellent.

Can NAFNet deblur photos where multiple types of blur are combined?

NAFNet handles a range of blur types individually — camera shake blur, moderate defocus, and motion blur from subject movement — all of which appear in its GoPro training data. Mixed blur (simultaneous camera shake and defocus, for example) is more challenging because it falls at the intersection of training examples rather than squarely within one category. For moderate mixed blur common in old family photographs, NAFNet typically produces useful improvement. For severe compound blur (extreme defocus combined with strong camera shake), results are more variable and the improvement may be partial rather than complete. The model's performance degrades gracefully rather than producing obvious artifacts in these cases — it produces a modestly improved image rather than a clearly damaged one, which is the appropriate behavior for an image restoration component used in a downstream pipeline.

Does NAFNet work as a standalone tool or does it need the full restoration pipeline?

NAFNet works effectively as a standalone denoising or deblurring tool for photographs that primarily suffer from grain or blur rather than multiple compound issues. If your photograph is well-preserved but has film grain from a high-ISO scan, denoising alone may be sufficient. Similarly, if a photograph is otherwise sharp and clear but has one area of camera shake blur, deblurring alone may address the issue. For most old family photographs that have accumulated multiple types of damage over decades — grain, fading, slight blur, and low resolution together — the full restoration pipeline (NAFNet + GFPGAN + Real-ESRGAN) produces substantially better results than any single component. ArtImageHub's old photo restoration at $4.99 one-time applies the full pipeline, while individual tools (photo denoiser, photo deblurrer) apply NAFNet-based processing as standalone steps.

Is NAFNet's simplified architecture a disadvantage in any way?

The simplified architecture is a genuine advantage for deployment speed and memory efficiency — NAFNet runs faster and requires less GPU memory than architecturally complex alternatives. The main potential disadvantage is reduced expressive capacity: very complex image restoration tasks that require highly context-dependent processing might theoretically benefit from more complex architectural components. In practice, NAFNet's benchmark performance shows that for the core restoration tasks of denoising and deblurring, the simplified architecture is not a meaningful limitation. The "Simple Baselines" paper demonstrated that NAFNet achieved state-of-the-art results on standard benchmarks while being architecturally simpler than previous state-of-the-art methods. For niche restoration tasks outside the standard denoising and deblurring domains, more specialized architectures may have advantages.