AdaIR: Adaptive All-in-One Image Restoration via Frequency Mining and Modulation
Yuning Cui, Syed Waqas Zamir, Salman Khan, Alois Knoll, Mubarak Shah, Fahad Shahbaz Khan
2025-01-27
Summary
This paper talks about AdaIR, a new method for fixing various types of image problems all at once. It uses something called frequency mining and modulation to make images clearer and better-looking, no matter what kind of issue they have.
What's the problem?
When we take pictures, lots of things can go wrong. The image might be blurry, noisy, hazy, or have rain in it. Usually, we need different tools to fix each of these problems. Some newer methods try to fix all these issues with one tool, but they don't look at how different problems affect different parts of the image's frequency information. This means they might miss important details that could help fix the image better.
What's the solution?
The researchers created AdaIR, which looks at both the regular image and its frequency information. It does this in three main steps. First, it finds important information in both low and high frequencies of the image. Then, it adjusts this information to make it more useful. Finally, it combines this adjusted information with the original image to make it clearer and better. This method can adapt to different types of image problems because it pays attention to how each problem affects different frequency parts of the image.
Why it matters?
This research matters because it could make it much easier to fix all kinds of image problems with just one tool. Instead of needing separate programs for fixing blurry photos, removing haze, or clearing up dark images, we could have one program that does it all. This could be really helpful for photographers, but also for things like self-driving cars or medical imaging, where clear images are super important. The researchers showed that their method works better than other current methods for lots of different image fixing tasks, which is pretty exciting for anyone who works with or enjoys looking at photos.
Abstract
In the image acquisition process, various forms of degradation, including noise, haze, and rain, are frequently introduced. These degradations typically arise from the inherent limitations of cameras or unfavorable ambient conditions. To recover clean images from degraded versions, numerous specialized restoration methods have been developed, each targeting a specific type of degradation. Recently, all-in-one algorithms have garnered significant attention by addressing different types of degradations within a single model without requiring prior information of the input degradation type. However, these methods purely operate in the spatial domain and do not delve into the distinct frequency variations inherent to different degradation types. To address this gap, we propose an adaptive all-in-one image restoration network based on frequency mining and modulation. Our approach is motivated by the observation that different degradation types impact the image content on different frequency subbands, thereby requiring different treatments for each restoration task. Specifically, we first mine low- and high-frequency information from the input features, guided by the adaptively decoupled spectra of the degraded image. The extracted features are then modulated by a bidirectional operator to facilitate interactions between different frequency components. Finally, the modulated features are merged into the original input for a progressively guided restoration. With this approach, the model achieves adaptive reconstruction by accentuating the informative frequency subbands according to different input degradations. Extensive experiments demonstrate that the proposed method achieves state-of-the-art performance on different image restoration tasks, including denoising, dehazing, deraining, motion deblurring, and low-light image enhancement. Our code is available at https://github.com/c-yn/AdaIR.