Self-Supervised Denoising Image Filter Based on Recursive Deep Neural Network Structure.

Sang-ug Kang,Changhee Kang

doi:10.3390/s21237827

Abstract

The purpose of this paper is to propose a novel noise removal method based on deep neural networks that can remove various types of noise without paired noisy and clean data. Because this type of filter generally has relatively poor performance, the proposed noise-to-blur-estimated clean (N2BeC) model introduces a stage-dependent loss function and a recursive learning stage for improved denoised image quality. The proposed loss function regularizes the existing loss function so that the proposed model can better learn image details. Moreover, the recursive learning stage provides the proposed model with an additional opportunity to learn image details. The overall deep neural network consists of three learning stages and three corresponding loss functions. We determine the essential hyperparameters via several simulations. Consequently, the proposed model showed more than 1 dB superior performance compared with the existing noise-to-blur model.

Highlights

Cameras and sensors in autonomous vehicles and outdoor vision systems, such as closed-circuit televisions and dashboard cameras, are rapidly becoming important.Information obtained from visual and miscellaneous sensors should be as accurate as possible, because erroneous information can compromise both safety and property
We propose a high-performance and self-supervised method without dataset problems by introducing a recursive learning stage and a stage-dependent objective function
We propose a recursive learning method and a stagedependent loss function

Summary

Introduction

Cameras and sensors in autonomous vehicles and outdoor vision systems, such as closed-circuit televisions and dashboard cameras, are rapidly becoming important. Information obtained from visual and miscellaneous sensors should be as accurate as possible, because erroneous information can compromise both safety and property. The internal process of obtaining an image from a real scene using a camera is very complicated and is always accompanied by noise for various reasons. Since the shape and pattern of noise are random and unpredictable, it is difficult to design an appropriate denoising filter. Sometimes noise is caused by the external environment rather than the camera itself, including raindrops, snowflakes and even captions in images. Various deep neural network approaches [1,2,3,4,5] have been proposed to remove such environmental noises

Objectives

Methods

Results

Conclusion