State-of-the-art approaches for image deconvolution problems, including modern deep learning architectures
M Makarkin, D Bratashov - Micromachines, 2021 - mdpi.com
In modern digital microscopy, deconvolution methods are widely used to eliminate a number
of image defects and increase resolution. In this review, we have divided these methods into …
of image defects and increase resolution. In this review, we have divided these methods into …
Joint face image restoration and frontalization for recognition
In real-world scenarios, many factors may harm face recognition performance, eg., large
pose, bad illumination, low resolution, blur and noise. To address these challenges …
pose, bad illumination, low resolution, blur and noise. To address these challenges …
On the unreasonable vulnerability of transformers for image restoration-and an easy fix
Following their success in visual recognition tasks, Vision Transformers (ViTs) are being
increasingly employed for image restoration. As a few recent works claim that ViTs for image …
increasingly employed for image restoration. As a few recent works claim that ViTs for image …
Improving feature stability during upsampling–spectral artifacts and the importance of spatial context
S Agnihotri, J Grabinski, M Keuper - European Conference on Computer …, 2025 - Springer
Pixel-wise predictions are required in a wide variety of tasks such as image restoration,
image segmentation, or disparity estimation. Common models involve several stages of data …
image segmentation, or disparity estimation. Common models involve several stages of data …
Convolutional deblurring for natural imaging
MS Hosseini, KN Plataniotis - IEEE Transactions on Image …, 2019 - ieeexplore.ieee.org
In this paper, we propose a novel design of image deblurring in the form of one-shot
convolution filtering that can directly convolve with naturally blurred images for restoration …
convolution filtering that can directly convolve with naturally blurred images for restoration …
Partial deconvolution with inaccurate blur kernel
Most non-blind deconvolution methods are developed under the error-free kernel
assumption, and are not robust to inaccurate blur kernel. Unfortunately, despite the great …
assumption, and are not robust to inaccurate blur kernel. Unfortunately, despite the great …
Image deblurring with a class-specific prior
A fundamental problem in image deblurring is to recover reliably distinct spatial frequencies
that have been suppressed by the blur kernel. To tackle this issue, existing image deblurring …
that have been suppressed by the blur kernel. To tackle this issue, existing image deblurring …
Non-blind optical degradation correction via frequency self-adaptive and finetune tactics
In mobile photography applications, limited volume constraints the diversity of optical
design. In addition to the narrow space, the deviations introduced in mass production cause …
design. In addition to the narrow space, the deviations introduced in mass production cause …
Defocusing parameter selection strategies based on PSF measurement for square-binary defocusing fringe projection profilometry
Y Wang, H Zhao, H Jiang, X Li - Optics Express, 2018 - opg.optica.org
Three-dimensional (3D) shape measurement system with binary defocusing technique can
perform high-speed and flexible measurements if binary fringe patterns are defocused by …
perform high-speed and flexible measurements if binary fringe patterns are defocused by …
Explicit ringing removal in image deblurring
In this paper, we present a simple yet effective image deblurring method to produce ringing-
free deblurred images. Our work is inspired by the observation that large-scale deblurring …
free deblurred images. Our work is inspired by the observation that large-scale deblurring …