Article
Self-Supervised Denoising Image Filter Based on Recursive
Deep Neural Network Structure
Changhee Kang and Sang-ug Kang *
Citation: Kang, C.; Kang, S.-u.
Self-Supervised Denoising Image
Filter Based on Recursive Deep
Neural Network Structure. Sensors
2021, 21, 7827. https://doi.org/
10.3390/s21237827
Academic Editors: Nunzio Cennamo,
Yangquan Chen, Subhas
Mukhopadhyay, M. Jamal Deen,
Junseop Lee and Simone Morais
Received: 7 October 2021
Accepted: 20 November 2021
Published: 24 November 2021
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4.0/).
Department of Computer Science, Sangmyung University, Seoul 03016, Korea; 202032028@sangmyung.kr
* Correspondence: sukang@smu.ac.kr; Tel.: +82-2-781-7588
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.
Keywords:
denoising filter; deep neural network; self-supervised learning; recursive training;
raindrop removal
1. Introduction
Recently, 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 pos-
sible, because erroneous information can compromise both safety and property. However,
the internal process of obtaining an image from a real scene using a camera is very compli-
cated 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. So, various deep neural
network approaches [1–5] have been proposed to remove such environmental noises.
There are two noise removal approaches, hand-crafted and deep neural network
approaches. First, hand-crafted approaches use various image features to remove noise.
Buades et al. [
6
] utilized the fact that natural images often exhibit repetitive local patterns
and many similar regions throughout the image. Therefore, similarity can be calculated by
calculating the L2 distance between the kernel region and any region of an image. Then,
the filtered value is obtained by computing the weighted average of similar regions, where
the weights are determined based on the similarity. Some transform-based methods have
been proposed by assuming that a clean image is sparsely represented in a transform
domain [
7
–
9
]. However, various types of images cannot be guaranteed to be well sparsely
represented with a single transformation. Elad et al. [
7
] proposed a dictionary learning
method. In this context, the dictionary is a collection of basic elements that can represent
an image as their linear combination. The dictionary is updated and improved using
the k-singular value decomposition (K-SVD) method for more appropriate sparse repre-
sentations. Therefore, a denoised image can be estimated from the sparse representation
using the final updated dictionary. However, this method consumes lots of computation
Sensors 2021, 21, 7827. https://doi.org/10.3390/s21237827 https://www.mdpi.com/journal/sensors
