Conventional deconvolution methods utilize hand-crafted image priors to constrain the optimization. While deep-learning-based methods have simplified the optimization by end-to-end training, they fail to generalize well to blurs unseen in the training dataset. Thus, training image-specific models is important for higher generalization. Deep image prior (DIP) provides an approach to optimize the weights of a randomly initialized network with a single degraded image by maximum a posteriori (MAP), which shows that the architecture of a network can serve as the hand-crafted image prior. Different from the conventional hand-crafted image priors that are statistically obtained, it is hard to find a proper network architecture because the relationship between images and their corresponding network architectures is unclear. As a result, the network architecture cannot provide enough constraint for the latent sharp image. This paper proposes a new variational deep image prior (VDIP) for blind image deconvolution, which exploits additive hand-crafted image priors on latent sharp images and approximates a distribution for each pixel to avoid suboptimal solutions. Our mathematical analysis shows that the proposed method can better constrain the optimization. The experimental results further demonstrate that the generated images have better quality than that of the original DIP on benchmark datasets. The source code of our VDIP is available at https://github.com/Dong-Huo/VDIP-Deconvolution.

翻译：常规解剖方法使用手工制作的图像前端限制优化。 虽然深层学习方法通过端到端培训简化了优化优化, 但无法在培训数据集中广泛推广, 无法在培训数据集中模糊可见。 因此, 培训图像特有模型对于更高的概括性很重要 。 深层图像前端( DIP) 提供了一种优化随机初始化网络的重量的方法, 以最深层图像为单一退化图像, 后端图像( MAP) 显示网络结构可以作为手制图像。 深层学习方法使网络结构与常规手工制作的图像前端不同, 但很难找到合适的网络结构, 因为图像和相应网络结构之间的关系不清楚。 因此, 网络结构无法为潜在锐化图像提供足够的限制。 本文提出在盲人图像解析前方( VDIP) 之前使用添加式手制图像前端图像, 并估计每个像组的分布, 以避免进一步的次优化的解决方案。 我们的数学分析显示, 原始的D- D- 版本图像质量比原始的模型能更好地限制数据源。