论文标题
非盲图像端到端的可解释学习
End-to-end Interpretable Learning of Non-blind Image Deblurring
论文作者
论文摘要
非盲图像去除术通常是作为线性最小二乘方格的问题，由自然先验在相应的夏普图片梯度上正式化，例如，可以通过使用Richardson固定点迭代的半界面分裂方法来解决，以进行最小值的更新和Auximal operator for Auxiliary Valitaine更新。我们建议使用（已知的）模糊和自然图像先验内核的近似逆滤波器来先决条件。与经典的FFT和共轭射门方法相比，使用卷积代替通用线性预处理可以在整个图像上共享非常有效的参数共享，并可以在准确性和/或速度上取得显着提高。更重要的是，所提出的体系结构很容易适应使用CNN嵌入的预处理和近端操作员。对于非盲图，可以从端到头来学会，并且在不均匀的情况下，这可以完全可以解释，并且其准确性匹配或超过最明显的算法。
Non-blind image deblurring is typically formulated as a linear least-squares problem regularized by natural priors on the corresponding sharp picture's gradients, which can be solved, for example, using a half-quadratic splitting method with Richardson fixed-point iterations for its least-squares updates and a proximal operator for the auxiliary variable updates. We propose to precondition the Richardson solver using approximate inverse filters of the (known) blur and natural image prior kernels. Using convolutions instead of a generic linear preconditioner allows extremely efficient parameter sharing across the image, and leads to significant gains in accuracy and/or speed compared to classical FFT and conjugate-gradient methods. More importantly, the proposed architecture is easily adapted to learning both the preconditioner and the proximal operator using CNN embeddings. This yields a simple and efficient algorithm for non-blind image deblurring which is fully interpretable, can be learned end to end, and whose accuracy matches or exceeds the state of the art, quite significantly, in the non-uniform case.
