Blind Image Deconvolution by Generative-based Kernel Prior and Initializer via Latent Encoding

• URL: http://arxiv.org/abs/2407.14816v1
• Date: Sat, 20 Jul 2024 09:23:56 GMT
• Title: Blind Image Deconvolution by Generative-based Kernel Prior and Initializer via Latent Encoding
• Authors: Jiangtao Zhang, Zongsheng Yue, Hui Wang, Qian Zhao, Deyu Meng,
• Abstract summary: Blind image encoding is a classic yet challenging problem in the field of image processing. Recent advances in deep image prior (DBID) have motivated a series of methods that demonstrate a-generative approaches. We propose a new pre-generative approach that better considers the prior modeling, and a generative approach for blur kernels.
• Score: 46.40894748268764
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Blind image deconvolution (BID) is a classic yet challenging problem in the field of image processing. Recent advances in deep image prior (DIP) have motivated a series of DIP-based approaches, demonstrating remarkable success in BID. However, due to the high non-convexity of the inherent optimization process, these methods are notorious for their sensitivity to the initialized kernel. To alleviate this issue and further improve their performance, we propose a new framework for BID that better considers the prior modeling and the initialization for blur kernels, leveraging a deep generative model. The proposed approach pre-trains a generative adversarial network-based kernel generator that aptly characterizes the kernel priors and a kernel initializer that facilitates a well-informed initialization for the blur kernel through latent space encoding. With the pre-trained kernel generator and initializer, one can obtain a high-quality initialization of the blur kernel, and enable optimization within a compact latent kernel manifold. Such a framework results in an evident performance improvement over existing DIP-based BID methods. Extensive experiments on different datasets demonstrate the effectiveness of the proposed method.

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