Deep Image Prior Denoising Model Using Relatively Clean Image Space Search

XU Shaoping; XIONG Minghai; ZHOU Changfei

doi:10.11999/JEIT240114

Volume 46 Issue 11

Nov. 2024

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XU Shaoping, XIONG Minghai, ZHOU Changfei. Deep Image Prior Denoising Model Using Relatively Clean Image Space Search[J]. Journal of Electronics & Information Technology, 2024, 46(11): 4229-4235. doi: 10.11999/JEIT240114

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XU Shaoping, XIONG Minghai, ZHOU Changfei. Deep Image Prior Denoising Model Using Relatively Clean Image Space Search[J]. Journal of Electronics & Information Technology, 2024, 46(11): 4229-4235. doi: 10.11999/JEIT240114

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Deep Image Prior Denoising Model Using Relatively Clean Image Space Search

doi: 10.11999/JEIT240114

School of Mathematics and Computer Sciences, Nanchang University, Nanchang 330031, China

Funds: The National Natural Science Foundation of China (62162043)

Received Date: 2024-02-28
Rev Recd Date: 2024-09-07

Available Online: 2024-09-29

Publish Date: 2024-11-10

Abstract

Abstract

Given that the performance of the Deep Image Prior (DIP) denoising model highly depends on the search space determined by the target image, a new improved denoising model called RS-DIP (Relatively clean image Space-based DIP) is proposed by comprehensively improving its network input, backbone network, and loss function.Initially, two state-of-the-art supervised denoising models are employed to preprocess two noisy images from the same scene, which are referred to as relatively clean images. Furthermore, these two relatively clean images are combined as the network input using a random sampling fusion method. At the same time, the noisy images are replaced with two relatively clean images, which serve as dual-target images. This strategy narrows the search space, allowing exploration of potential images that closely resemble the ground-truth image. Finally, the multi-scale U-shaped backbone network in the original DIP model is simplified to a single scale. Additionally, the inclusion of Transformer modules enhances the network’s ability to effectively model distant pixels. This augmentation bolsters the model’s performance while preserving the network’s search capability. Experimental results demonstrate that the proposed denoising model exhibits significant advantages over the original DIP model in terms of both denoising effectiveness and execution efficiency. Moreover, regarding denoising effectiveness, it surpasses mainstream supervised denoising models.
- Deep Image Prior (DIP),
- Denoising performance,
- Relatively clean images,
- Random sampling fusion,
- Dual-target image,
- Transformer

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References(20)

References

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