WanlingLiuQianDong

Introduction

The project is to study and find a robust machine learning method for blind motion deblurring. Motion blur happens when there is a relative motion between a camera and a scene during image exposure time. Reducing exposure time is a fairly straight-forward way to solve the problem, but the method can increase image noise significantly especially in dark scenes. Instead of doing it online, machine learning is able to learn and remove the blurring kernel offline without the kernel function information. We will apply different machine learning models, mainly focusing on CNN (Convolutional Neural Network) and GAN (Generative Adversarial Network), to solve the motion deblurring problem. By comparing their pros and cons, we will choose the best model with highest performance and robustness, and combine or modify the existing CNN and GAN models to better improve the quality of deblurred images. We calculated PSNR (Peak Signal to Noise Ratio), SSIM (Structural Similarity) and MSE (Mean Square Error) to evaluate our results. We used the GOPRO dataset, which is widely used for training and testing the models for motion deblurring. The dataset can be found here.

Background

There are two kinds of blurry images, one is blind images, knowing the sharp images while not knowing the source of blurring, the other one is non-blind, knowing both the sharp image and blurring source. In this project, we will focus on the blind images. To address the blurry image problem, CNN and GAN are commonly used. For example, this paper (Links to an external site.) used multi-layer perceptron (MLP) for non-blind images, this work (Links to an external site.) designed a deep multi-scale CNN for dynamic scene deblurring, and this repo (Links to an external site.) applied GAN for blind images.

Methods

• GAN model
• CNN model
• Metrics

1. MSE
2. PSNR
3. SSIM

Results

• GAN
• CNN

Appendix

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