Conditional Expressions for Blind Deconvolution: Derivative form
S. Aogaki, I. Moritani, T. Sugai, F. Takeutchi, F. M. Toyama
Abstract
We developed novel conditional expressions (CEs) for Lane and Bates' blind deconvolution. The CEs are given in term of the derivatives of the zero-values of the z-transform of given images. The CEs make it possible to automatically detect multiple blur convolved in the given images all at once without performing any analysis of the zero-sheets of the given images. We illustrate the multiple blur-detection by the CEs for a model image
Create a lesson
Related papers
UrbanGround: From Local Perception to Spatial Agency in a Real-Scale City
Tianjie Ju, Zheng Wu, Yueqing Sun et al.
Retrieval Heads Meet Vision: Uncovering How VLMs Locate and Extract Visual Information
Chanho Park, Daehyeon Choi, Jihyun Lee et al.
Reconstructing Humans and Objects in Interaction using Large Reconstruction Models
Agniv Chatterjee, Georgios Pavlakos
LeVJEPA: Efficient & Scalable Video Pretraining without the Heuristics
Lukas Kuhn, Lucas Maes, Giuseppe Serra et al.
Successive Capacity Growth: Task-Complexity-Driven Width and Depth Expansion for Vision Transformer Encoders in JEPA World Models
Frederik Berenz
KnockGS:interaction-Grounded Calibrationof Physical Gaussian Representations
Chenchen Ge, Hanwen Shen, Bowen Jing et al.