Sun Shuhan, Duan Lizhen, Xu Zhiyong, Zhang Jianlin
Key Laboratory of Optical Engineering, Chinese Academy of Sciences, Chengdu 610209, China.
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China.
Sensors (Basel). 2021 May 17;21(10):3484. doi: 10.3390/s21103484.
Blind image deblurring, also known as blind image deconvolution, is a long-standing challenge in the field of image processing and low-level vision. To restore a clear version of a severely degraded image, this paper proposes a blind deblurring algorithm based on the sigmoid function, which constructs novel blind deblurring estimators for both the original image and the degradation process by exploring the excellent property of sigmoid function and considering image derivative constraints. Owing to these symmetric and non-linear estimators of low computation complexity, high-quality images can be obtained by the algorithm. The algorithm is also extended to image sequences. The sigmoid function enables the proposed algorithm to achieve state-of-the-art performance in various scenarios, including natural, text, face, and low-illumination images. Furthermore, the method can be extended naturally to non-uniform deblurring. Quantitative and qualitative experimental evaluations indicate that the algorithm can remove the blur effect and improve the image quality of actual and simulated images. Finally, the use of sigmoid function provides a new approach to algorithm performance optimization in the field of image restoration.
盲图像去模糊，也称为盲图像反卷积，是图像处理和低级视觉领域长期存在的挑战。为了恢复严重退化图像的清晰版本，本文提出了一种基于 sigmoid 函数的盲去模糊算法，该算法通过探索 sigmoid 函数的优良特性并考虑图像导数约束，为原始图像和退化过程构建了新颖的盲去模糊估计器。由于这些具有低计算复杂度的对称和非线性估计器，该算法可以获得高质量的图像。该算法还扩展到了图像序列。sigmoid 函数使所提出的算法在各种场景下都能实现领先的性能，包括自然图像、文本图像、面部图像和低光照图像。此外，该方法可以自然地扩展到非均匀去模糊。定量和定性实验评估表明，该算法可以消除模糊效果并提高实际图像和模拟图像的质量。最后，sigmoid 函数的使用为图像恢复领域的算法性能优化提供了一种新方法。
