# Distributed Deblurring of Large Images of Wide Field-Of-View

## TL;DR

This paper introduces a distributed image deblurring algorithm that efficiently processes extremely large images by dividing the task among multiple nodes, maintaining quality comparable to centralized methods while reducing resource requirements.

## Contribution

It presents a novel distributed deblurring approach for large images, addressing memory limitations and enabling efficient processing across multiple nodes with maintained image quality.

## Key findings

- Achieves deblurring quality similar to centralized methods.
- Enables processing of gigapixel images with distributed computing.
- Reduces computational resource requirements for large-scale image deblurring.

## Abstract

Image deblurring is an economic way to reduce certain degradations (blur and noise) in acquired images. Thus, it has become essential tool in high resolution imaging in many applications, e.g., astronomy, microscopy or computational photography. In applications such as astronomy and satellite imaging, the size of acquired images can be extremely large (up to gigapixels) covering wide field-of-view suffering from shift-variant blur. Most of the existing image deblurring techniques are designed and implemented to work efficiently on centralized computing system having multiple processors and a shared memory. Thus, the largest image that can be handle is limited by the size of the physical memory available on the system. In this paper, we propose a distributed nonblind image deblurring algorithm in which several connected processing nodes (with reasonable computational resources) process simultaneously different portions of a large image while maintaining certain coherency among them to finally obtain a single crisp image. Unlike the existing centralized techniques, image deblurring in distributed fashion raises several issues. To tackle these issues, we consider certain approximations that trade-offs between the quality of deblurred image and the computational resources required to achieve it. The experimental results show that our algorithm produces the similar quality of images as the existing centralized techniques while allowing distribution, and thus being cost effective for extremely large images.