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Existing works on motion deblurring either ignore the effects of depth-dependent blur or work with the assumption of a multi-layered scene wherein each layer is modeled in the form of fronto-parallel plane. In this work, we consider the…

Computer Vision and Pattern Recognition · Computer Science 2022-02-08 Kuldeep Purohit , Subeesh Vasu , M. Purnachandra Rao , A. N. Rajagopalan

Numerical and experimental turbulence simulations are nowadays reaching the size of the so-called big data, thus requiring refined investigative tools for appropriate statistical analyses and data mining. We present a new approach based on…

Fluid Dynamics · Physics 2017-01-05 Stefania Scarsoglio , Giovanni Iacobello , Luca Ridolfi

We propose and demonstrate a new phase retrieval method for imaging through random media. Although methods to recover the Fourier amplitude through random distortions are well established, recovery of the Fourier phase has been a more…

Optics · Physics 2021-05-14 Byungjae Hwang , Taeseong Woo , Cheolwoo Ahn , Jung-Hoon Park

Recovering high-fidelity images of the night sky from blurred observations is a fundamental problem in astronomy, where traditional methods typically fall short. In ground-based astronomy, combining multiple exposures to enhance…

Instrumentation and Methods for Astrophysics · Physics 2025-09-04 Yashil Sukurdeep , Fausto Navarro , Tamás Budavári

Atmospheric turbulence significantly degrades long-range imaging by introducing geometric warping and exposure-time-dependent blur, which adversely affects both visual quality and the performance of high-level vision tasks. Existing methods…

Computer Vision and Pattern Recognition · Computer Science 2026-03-04 Junwei Zeng , Dong Liang , Sheng-Jun Huang , Kun Zhan , Songcan Chen

We develop a straightforward analytical framework for the propagation of spatial light modes through a turbulent atmosphere. Built upon the split-step approach with the mode-based optical field representation, it directly assesses how…

Quantum Physics · Physics 2026-05-29 Konstantin Kravtsov

Image deraining have have gained a great deal of attention in order to address the challenges posed by the effects of harsh weather conditions on visual tasks. While convolutional neural networks (CNNs) are popular, their limitations in…

Computer Vision and Pattern Recognition · Computer Science 2024-03-13 Sijin He , Guangfeng Lin

Atmospheric turbulence is a serious problem for traditional optical imaging, especially for satellite and aircraft-to-ground imaging. Here, we report a novel and practical phenomenon in which turbulence-free images can be reconstructed on a…

Image and Video Processing · Electrical Eng. & Systems 2021-09-10 Deyang Duan , Yunjie Xia

Currently, transformer-based algorithms are making a splash in the domain of image deblurring. Their achievement depends on the self-attention mechanism with CNN stem to model long range dependencies between tokens. Unfortunately, this…

Computer Vision and Pattern Recognition · Computer Science 2025-01-16 Xingchi Chen , Xiuyi Jia , Zhuoran Zheng

Super-resolution (SR) techniques based on deep learning have recently emerged as a promising approach to enhance the spatial resolution of computational fluid dynamics simulations while containing computational cost. In this paper, we…

Fluid Dynamics · Physics 2026-04-13 Armin Sheidani , Michele Girfoglio , Annalisa Quaini , Gianluigi Rozza

To elucidate the attenuation mechanism of wall-bounded turbulence due to heavy small particles, we conduct direct numerical simulations (DNS) of turbulent channel flow laden with finite-size solid particles. When particles cannot follow the…

Fluid Dynamics · Physics 2025-07-16 Yutaro Motoori , Susumu Goto

Image deblurring is an ill-posed problem with multiple plausible solutions for a given input image. However, most existing methods produce a deterministic estimate of the clean image and are trained to minimize pixel-level distortion. These…

Computer Vision and Pattern Recognition · Computer Science 2021-12-30 Jay Whang , Mauricio Delbracio , Hossein Talebi , Chitwan Saharia , Alexandros G. Dimakis , Peyman Milanfar

Separating turbulent fluctuations from coherent large-scale background flows is a longstanding challenge in the analysis of numerical simulations and astronomical observations. Traditional approaches commonly rely on decomposition-based…

Fluid Dynamics · Physics 2026-05-27 Ji-Hoon Ha , Elena S. Volnova

Using convolutional neural networks for 360images can induce sub-optimal performance due to distortions entailed by a planar projection. The distortion gets deteriorated when a rotation is applied to the 360image. Thus, many researches…

Computer Vision and Pattern Recognition · Computer Science 2022-02-14 Sungmin Cho , Raehyuk Jung , Junseok Kwon

Removing spatially variant motion blur from a blurry image is a challenging problem as blur sources are complicated and difficult to model accurately. Recent progress in deep neural networks suggests that kernel free single image deblurring…

Computer Vision and Pattern Recognition · Computer Science 2019-11-26 Shuang Zhang , Ada Zhen , Robert L. Stevenson

We experimentally study a nonlinear optical approach to selective manipulation and detection of structured images mixed with turbulent noise. Unlike any existing adaptive-optics method by applying compensating modulation directly on the…

Optics · Physics 2020-10-21 He Zhang , Santosh Kumar , Yu-Ping Huang

Numerical simulation of atmospheric turbulence is one of the biggest bottlenecks in developing computational techniques for solving the inverse problem in long-range imaging. The classical split-step method is based upon numerical wave…

Image and Video Processing · Electrical Eng. & Systems 2022-10-14 Nicholas Chimitt , Xingguang Zhang , Zhiyuan Mao , Stanley H. Chan

We report the experimental characterization of free-surface deformations generated by three-dimensional homogeneous and isotropic turbulence. Using Fourier transform profilometry in a jet-forced turbulent tank, we perform spatiotemporal…

Fluid Dynamics · Physics 2026-05-14 Michaël Berhanu , Eric Falcon

We describe a procedure by which a long ($\gtrsim 1\,\mathrm{km}$) optical path through atmospheric turbulence can be experimentally simulated in a controlled fashion and scaled down to distances easily accessible in a laboratory setting.…

Atmospheric turbulence severely limits the coupling of received optical wavefronts into single-mode fibers in satellite-to-ground free-space optical links. Spatial demultiplexing receivers address this challenge by distributing the incoming…

Optics · Physics 2026-03-20 Nicolas Barré