Abstract

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.

Library of Congress Subject Headings

Imaging systems--Image quality; Spectrum analysis--Deconvolution; Deep learning (Machine learning)

Publication Date

12-1-2023

Document Type

Thesis

Student Type

Graduate

Degree Name

Imaging Science (MS)

Department, Program, or Center

Chester F. Carlson Center for Imaging Science

College

College of Science

Advisor

Grover Swartzlander

Advisor/Committee Member

Dimah Dera

Advisor/Committee Member

Carl Salvaggio

Campus

RIT – Main Campus

Plan Codes

IMGS-MS

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