# Efficient Blind Deblurring under High Noise Levels

## TL;DR

This paper introduces an efficient blind deblurring method that effectively handles high noise levels by adapting kernel estimation and incorporating denoising, achieving high-quality results with reduced computational cost.

## Contribution

It demonstrates that existing kernel estimation techniques can be adapted for high noise scenarios and improves deconvolution by integrating denoising, offering a faster alternative to complex methods.

## Key findings

- Kernel estimation methods based on $
0$ gradient prior can be adapted for high noise.
- Denoising before deconvolution improves image restoration quality.
- Proposed method achieves comparable results to more computationally intensive approaches.

## Abstract

The goal of blind image deblurring is to recover a sharp image from a motion blurred one without knowing the camera motion. Current state-of-the-art methods have a remarkably good performance on images with no noise or very low noise levels. However, the noiseless assumption is not realistic considering that low light conditions are the main reason for the presence of motion blur due to requiring longer exposure times. In fact, motion blur and high to moderate noise often appear together. Most works approach this problem by first estimating the blur kernel $k$ and then deconvolving the noisy blurred image. In this work, we first show that current state-of-the-art kernel estimation methods based on the $\ell_0$ gradient prior can be adapted to handle high noise levels while keeping their efficiency. Then, we show that a fast non-blind deconvolution method can be significantly improved by first denoising the blurry image. The proposed approach yields results that are equivalent to those obtained with much more computationally demanding methods.