arXiv:2607.02952v1 Announce Type: cross Abstract: Convolutional Neural Networks (CNNs) achieve strong denoising performance by exploiting spatial context from neighboring pixels. Deep Image Prior (DIP) leverages this property to restore images from a single noisy input without requiring large datasets. However, the over-parameterized architecture of DIP often leads to noise fitting during optimization. In this paper, we propose Pool-DIP, a convolution-free architecture that incorporates pooling-based contrast modeling to capture spatial context efficiently. Pool-DIP improves denoising performa
The paper leverages recent advancements in neural network architectures to address long-standing challenges in image restoration, particularly the limitations of traditional CNNs and older DIP methods.
Improving image denoising with more efficient and convolution-free methods can significantly impact AI applications requiring high-quality visual data, from medical imaging to autonomous systems, without incurring high computational costs or large datasets.
The proposed Pool-DIP architecture offers a more efficient way to perform image restoration from single noisy inputs, potentially leading to more robust and less data-hungry computer vision models.
- · Computer Vision Researchers
- · Medical Imaging
- · Autonomous Vehicle Developers
- · AI hardware manufacturers
- · Traditional CNN-based DIP methods
- · Image restoration techniques reliant on large datasets
More efficient and accurate image denoisers become widely available for various applications.
Reduced computational requirements for image denoising could enable deployment on edge devices and in resource-constrained environments.
The success of convolution-free methods might accelerate the exploration of alternative architectural paradigms for other computer vision tasks, potentially shifting research focus away from pure CNNs.
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Read at arXiv cs.AI