# Blur Removal via Blurred-Noisy Image Pair

## TL;DR

This paper introduces a kernel-free deblurring approach using a blurred-noisy image pair, leveraging Gaussian mixture models and optical flow to effectively restore sharp images in complex blur scenarios.

## Contribution

A novel deblurring method that does not require blur kernel estimation, utilizing image pairs with different noise and shutter speed characteristics.

## Key findings

- Outperforms state-of-the-art methods in robustness and visual quality
- Effective in handling complex space-variant and space-invariant blur
- Validated on synthetic and real-world datasets

## Abstract

Complex blur such as the mixup of space-variant and space-invariant blur, which is hard to model mathematically, widely exists in real images. In this paper, we propose a novel image deblurring method that does not need to estimate blur kernels. We utilize a pair of images that can be easily acquired in low-light situations: (1) a blurred image taken with low shutter speed and low ISO noise; and (2) a noisy image captured with high shutter speed and high ISO noise. Slicing the blurred image into patches, we extend the Gaussian mixture model (GMM) to model the underlying intensity distribution of each patch using the corresponding patches in the noisy image. We compute patch correspondences by analyzing the optical flow between the two images. The Expectation Maximization (EM) algorithm is utilized to estimate the parameters of GMM. To preserve sharp features, we add an additional bilateral term to the objective function in the M-step. We eventually add a detail layer to the deblurred image for refinement. Extensive experiments on both synthetic and real-world data demonstrate that our method outperforms state-of-the-art techniques, in terms of robustness, visual quality, and quantitative metrics.