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Shift Variant Image Deconvolution Us...

Ghosh, Arnab.

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Shift Variant Image Deconvolution Using Deep Learning.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Shift Variant Image Deconvolution Using Deep Learning./
作者:	Ghosh, Arnab.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
面頁冊數:	71 p.
附註:	Source: Masters Abstracts International, Volume: 85-06.
Contained By:	Masters Abstracts International85-06.
標題:	Computer science. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30818612
ISBN:	9798381185393

Shift Variant Image Deconvolution Using Deep Learning.
Ghosh, Arnab.

Shift Variant Image Deconvolution Using Deep Learning. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 71 p.

Source: Masters Abstracts International, Volume: 85-06.

Thesis (M.S.)--Rochester Institute of Technology, 2023.

Image Deconvolution is a well-studied problem that seeks to restore the original sharp image from a blurry image formed in the imaging system. The Point Spread function(PSF) of a particular system can be used to infer the original sharp image given the blurred image. However, such a problem is usually simplified by making the shift-invariant assumption over the Field of View (FOV). Realistic systems are shift-variant; the optical system's point spread function depends on the position of the object point from the principal axis. For example, asymmetrical lenses can cause space variant aberration. In this paper, we first simulate our shift-variant aberrations by generating Point Spread Functions using the Seidel Aberration polynomial and use a shift-variant forward blur model to generate our shift-variant blurred image pairs. We then introduce, ShiVaNet. It is a two-stage architecture that builds upon the Learnable Wiener Deconvolution block as described in Yanny, Monakhova, Shuai, and Waller (Yanny et al.) by introducing Simplified Channel Attention and Transpose Attention to improve the performance of the module. We also devise a novel UNet refinement block by fusing a ConvNext-V2 block with Channel Attention and coupling with Transposed Attention Zamir, Arora, Khan, Hayat, Khan, and Yang (Zamir et al.). Our model performs better than state-of-the-art restoration models by a factor of 0.2 dB Peak Signal to Noise Ratio.

ISBN: 9798381185393Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Aberration correction

Shift Variant Image Deconvolution Using Deep Learning.
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300 $a 71 p.
500 $a Source: Masters Abstracts International, Volume: 85-06.
500 $a Advisor: Swartzlander, Grover.
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520 $a Image Deconvolution is a well-studied problem that seeks to restore the original sharp image from a blurry image formed in the imaging system. The Point Spread function(PSF) of a particular system can be used to infer the original sharp image given the blurred image. However, such a problem is usually simplified by making the shift-invariant assumption over the Field of View (FOV). Realistic systems are shift-variant; the optical system's point spread function depends on the position of the object point from the principal axis. For example, asymmetrical lenses can cause space variant aberration. In this paper, we first simulate our shift-variant aberrations by generating Point Spread Functions using the Seidel Aberration polynomial and use a shift-variant forward blur model to generate our shift-variant blurred image pairs. We then introduce, ShiVaNet. It is a two-stage architecture that builds upon the Learnable Wiener Deconvolution block as described in Yanny, Monakhova, Shuai, and Waller (Yanny et al.) by introducing Simplified Channel Attention and Transpose Attention to improve the performance of the module. We also devise a novel UNet refinement block by fusing a ConvNext-V2 block with Channel Attention and coupling with Transposed Attention Zamir, Arora, Khan, Hayat, Khan, and Yang (Zamir et al.). Our model performs better than state-of-the-art restoration models by a factor of 0.2 dB Peak Signal to Noise Ratio.
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653 $a Computational imaging
653 $a Deblurring
653 $a Deep learning
653 $a Image restoration
653 $a Seidel polynomials
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