Using self-supervised learning to denoise images

Images are part of our daily lives, from the photos we take to the videos recorded by cameras in public places. They contain insightful information, and cutting-edge technology makes it possible to analyze and interpret this data. 

In particular, computer vision, a branch of artificial intelligence (AI), enables machines to process visual information and understand what they see, much like humans do. However, in real-world applications, images are often far from perfect. 

Image noise caused by rain, dust, low light, or sensor limitations can hide important details, making it harder for Vision AI models to detect objects or interpret scenes accurately. Image denoising helps reduce this noise, making it possible for Vision AI models to see details more clearly and make better predictions.

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Traditionally, image denoising has relied on supervised learning, where models are trained using pairs of noisy and clean images to learn how to remove noise. However, collecting perfectly clean reference images isn’t always practical.

To tackle this challenge, researchers have developed self-supervised image denoisers. They aim to train AI models to learn directly from the data, creating their own learning signals to remove noise and keep important details without needing clean reference images.

In this article, we’ll take a closer look at self-supervised image denoisers, how they work, the key techniques behind them, and their real-world applications. Let’s get started!

What is self-supervised image denoising?

Noisy images can make it difficult for Vision AI models to interpret what’s in a picture. A photo taken in low-light conditions, for instance, may appear grainy or blurred, hiding subtle features that help a model identify objects accurately.

In supervised learning–based denoising, models are trained using pairs of images, one noisy and one clean, to learn how to remove unwanted noise. While this approach works well, collecting perfectly clean reference data is often time-consuming and difficult in real-world scenarios.

That’s why researchers have turned to self-supervised image denoising. Self-supervised image denoising builds on the concept of self-supervised learning, where models teach themselves by creating their own learning signals from the data.  

Since this method doesn’t depend on large labeled datasets, self-supervised denoising is faster, more scalable, and easier to apply across domains such as low-light photography, medical imaging, and satellite image analysis, where clean reference images are often unavailable.

Instead of relying on clean reference images, this approach trains directly on noisy data by predicting masked pixels or reconstructing missing parts. Through this process, the model learns to tell the difference between meaningful image details and random noise, leading to clearer and more accurate outputs. 

While it might seem similar to unsupervised learning, self-supervised learning is actually a special case of it. The key distinction is that in self-supervised learning, the model creates its own labels or training signals from the data to learn a specific task. In contrast, unsupervised learning focuses on finding hidden patterns or structures in data without any explicit task or predefined goal.

Learning strategies in self-supervised denoising

With respect to self-supervised denoising, there are several ways learning happens. Some self-supervised denoise models fill in masked or missing pixels, while others compare multiple noisy versions of the same image to find consistent details. 

For example, a popular method known as blind-spot learning focuses on training the denoiser model to ignore the pixel it is reconstructing and rely on the surrounding context instead. Over time, the model rebuilds high-quality images while preserving essential textures, edges, and colors.

How self-supervised learning works to remove noise

Next, we’ll explore the process behind how self-supervised learning removes noise. 

The process of self-supervised denoising typically begins by feeding noisy images into the denoising model. The model analyzes nearby pixels to estimate what each unclear or masked pixel should look like, gradually learning to tell the difference between noise and real visual details.

Consider an image of a dark, grainy sky. The model looks at nearby stars and surrounding patterns to predict what each noisy patch should look like without the noise. By repeating this process across the entire image, it learns to separate random noise from meaningful features, producing a clearer and more accurate result.

In other words, the model predicts a cleaner version of the image based on context, without ever needing a perfectly clean reference. This process can be implemented using different types of models, each with unique strengths in handling noise. 

Types of models used for self-supervised image noise reduction

Here’s a quick look at the types of models commonly used for self-supervised image denoising:

• Convolutional Neural Networks (CNNs): CNNs are deep learning models designed to recognize patterns in small regions of an image. They scan images using filters to detect edges, shapes, and textures. In self-supervised denoising, they often use blind-spot techniques, where the target pixel is excluded from the input so the model predicts its value based only on surrounding pixels. This helps the model avoid copying noise and instead infers cleaner details.
• Autoencoders: Autoencoders are neural networks that learn to compress and reconstruct data. They first reduce an image into a smaller representation (encoding) and then rebuild it (decoding). In the process, they learn to capture important visual features such as shapes and textures while filtering out random noise and irrelevant details.
• Transformer-based models: Transformers are models originally developed for natural language processing but now widely used for vision tasks. They process the entire image at once, learning how different regions relate to each other. This global perspective allows them to preserve fine details and structural consistency, even in complex or high-resolution images.

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Training these models with images taken in different lighting and ISO settings helps them work well in many real-world situations. In digital cameras, ISO settings control how much the camera brightens the image by amplifying the signal it receives. 

A higher ISO makes photos brighter in dark places but also increases noise and reduces detail. By learning from images taken at different ISO levels, the models get better at telling real details apart from noise, leading to clearer and more accurate results.

How does a denoiser learn what’s noise and what’s real?

Denoisers learn to tell noise from real image details through different training techniques, which are separate from the model types used for denoising. Model types such as CNNs, autoencoders, and transformers describe the structure of the network and how it processes visual information.

Training techniques, on the other hand, define how the model learns. Some methods use context-based prediction, where the model fills in missing or masked pixels by using information from nearby areas. 

Others use reconstruction-based learning, where the model compresses an image into a simpler form and then rebuilds it, helping it recognize meaningful structures like edges and textures while filtering out random noise.

Together, the model type and the training technique determine how effectively a denoiser can clean images. By combining the right architecture with the right learning approach, self-supervised denoisers can adapt to many types of noise and produce clearer, more accurate images even without clean reference data.

Key techniques in self-supervised AI image denoising

Here are some of the most widely used training techniques that enable effective self-supervised image denoising:

• Noise2Noise: This method trains a model using two noisy versions of the same image. Since the noise in each version is random, the model learns to focus on consistent details that represent the real image and ignore the noise. It works best when multiple noisy captures of the same scene are available, such as in burst photography or medical and scientific imaging.
• Noise2Void or Noise2Self: These techniques train on a single noisy image by hiding (masking) a pixel and asking the model to predict its value based on surrounding pixels. This prevents the model from simply copying noisy data and helps it learn the natural structure of images. They are especially useful when only one noisy image is available, such as in microscopy, astronomy, or low-light photography.
• Blind-spot networks: They are specially designed so the model can’t see the pixel it is reconstructing. Instead, it relies on information from the surrounding area to estimate what that pixel should look like. This makes noise removal more accurate and unbiased, and they are often combined with Noise2Void or Noise2Self methods in pixel-wise denoising tasks.
• Masked Autoencoders (MAE): In this approach, parts of an image are hidden, and the model learns to reconstruct the missing areas. By doing this, it learns both fine details and overall structure, helping it distinguish real content from noise. Masked autoencoders are especially effective for high-resolution or complex images where understanding the broader context improves restoration.

Evaluating image denoiser systems

Image denoising is a careful balance between two goals: reducing noise and keeping fine details intact. Too much denoising can make an image look soft or blurry, while too little can leave unwanted grain or artifacts behind.

To understand how well a model strikes this balance, researchers use evaluation metrics that measure both image clarity and detail preservation. These metrics show how well a model cleans up an image without losing important visual information. 

Here are common evaluation metrics that help measure image quality and denoising performance:

• Mean Squared Error (MSE): It measures the average squared difference between the original and denoised images. It highlights how close the output is to the original on a pixel level. Lower MSE values mean fewer errors and a more accurate result.
  
• Peak Signal-to-Noise Ratio (PSNR): This metric compares the strength of the original image signal to the remaining noise, expressed in decibels. It’s used to see how much of the original detail has been kept after denoising. Higher PSNR values mean clearer, higher-quality images.
  
• Structural Similarity Index Measure (SSIM): SSIM assesses the structure, brightness, and contrast to evaluate the similarity between the denoised image and the original. It focuses on how humans see images, not just raw numbers. Higher SSIM scores mean the image looks more natural and true to the original.
  
• Perceptual metrics: These metrics use deep learning models to judge how realistic and natural an image looks. Instead of comparing individual pixels, they focus on overall appearance, texture, and visual similarity. In most cases, lower scores mean the image looks closer to the original and more visually pleasing to humans.

Applications of self-supervised denoising

Now that we have a better understanding of what denoising is, let’s explore how self-supervised image denoising is applied in real-world scenarios.

Using self-supervised denoising in astrophotography

Taking clear photos of stars and galaxies isn’t easy. The night sky is dark, so cameras often require long exposure times, which can introduce unwanted noise. This noise can blur fine cosmic details and make faint signals harder to detect

Traditional denoising tools can help reduce noise, but they often remove important details along with it. Self-supervised denoising offers a smarter alternative. By learning directly from noisy images, the AI model can recognize patterns that represent real features and separate them from random noise.

The result is much clearer images of celestial objects like stars, galaxies, and the Sun, revealing faint details that might otherwise go unnoticed. It can also enhance subtle astronomical features, improving image clarity and making data more useful for scientific research.

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Self-supervised denoising for medical imaging

Medical scans like MRIs, CTs, and microscopy images often pick up noise that can make small details harder to see. This can be a problem when doctors need to spot early signs of illness or track changes over time. 

Image noise can come from patient movement, low signal strength, or limits on how much radiation can be used. To make medical scans clearer, researchers have explored self-supervised denoising methods like Noise2Self and other similar approaches. 

These models are trained directly on noisy brain MRI images, learning the noise patterns on their own and cleaning them up without needing perfectly clear examples. The processed images showed sharper textures and better contrast, making fine structures easier to identify. Such AI-powered denoisers streamline the workflow in diagnostic imaging and improve real-time analysis efficiency.

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Enhancing vision systems with self-supervised denoising

In most cases, denoising has a significant impact across a wide range of computer vision applications. By removing unwanted noise and distortions, it produces cleaner and more consistent input data for Vision AI models to process.

Clearer images lead to improved performance in computer vision tasks such as object detection, instance segmentation, and image recognition. Here are some examples of applications where Vision AI models, such as Ultralytics YOLO11 and Ultralytics YOLO26, can benefit from denoising:

• Industrial inspection: Denoising drives more accurate detection of surface defects or anomalies in manufacturing environments, leading to improved quality control.
• Autonomous driving and navigation: It enhances object and obstacle detection in challenging conditions such as low light, rain, or fog, improving overall safety and reliability.
• Surveillance and security: Denoising improves image quality in low-light or high-compression video feeds, allowing for better identification and tracking of objects or people.
• Underwater imaging: Denoising reduces scattering and light distortion, enhancing visibility and object recognition in turbid underwater conditions.

Pros and cons of self-supervised denoising

Here are some key benefits of using self-supervised denoising in imaging systems:

• Noise adaptability: Self-supervised denoising methods can learn directly from noisy data without requiring paired clean references. This makes them highly adaptable to a wide range of real-world noise levels and types, such as sensor noise, motion blur, or environmental interference.
• Detail preservation: When well-designed, these models preserve fine textures and edges that are essential for accurate image interpretation. Approaches such as blind-spot networks and masking-based learning help maintain structural information while reducing noise.
• Less pre-processing: By learning to map noisy inputs to clean representations using only the available data, the model minimizes the need for manual filtering, handcrafted denoising algorithms, or curated training datasets.

Despite its benefits, self-supervised denoising also comes with certain limitations. Here are a few factors to consider:

• Computational requirements: Deep neural architectures used for self-supervised denoising, especially transformer-based models, can require substantial computational power and memory resources compared to traditional filtering techniques.
• Model design complexity: Achieving optimal results requires careful selection of model settings, such as the masking strategy and loss function, which can vary across different noise types.
• Evaluation challenges: Common image quality metrics don’t always match how natural or realistic a denoised image looks, so visual or task-specific checks are often needed.

Key takeaways

Self-supervised denoising helps AI models learn directly from noisy images, producing clearer results while preserving fine details. It works effectively across a variety of challenging scenarios, such as low-light, high ISO, and detailed imagery. As AI continues to evolve, such techniques will likely play an essential role in various computer vision applications. 

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