APSIPA Transactions on Signal and Information Processing > Vol 8 > Issue 1

Noise bias compensation for tone mapped noisy image using prior knowledge

Sayaka Minewaki, National Institute of Technology, Japan, minewaki@info.yuge.ac.jp , Taichi Yoshida, The University of Electro-Communications, Japan, Yoshinori Takei, National Institute of Technology, Japan, Masahiro Iwahashi, Nagaoka University of Technology, Japan, Hitoshi Kiya, Tokyo Metropolitan University, Japan
 
Suggested Citation
Sayaka Minewaki, Taichi Yoshida, Yoshinori Takei, Masahiro Iwahashi and Hitoshi Kiya (2019), "Noise bias compensation for tone mapped noisy image using prior knowledge", APSIPA Transactions on Signal and Information Processing: Vol. 8: No. 1, e3. http://dx.doi.org/10.1017/ATSIP.2018.29

Publication Date: 04 Jan 2019
© 2019 Sayaka Minewaki, Taichi Yoshida, Yoshinori Takei, Masahiro Iwahashi and Hitoshi Kiya
 
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Keywords
DenoisingImages
 

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In this article:
I. INTRODUCTION 
II. PROBLEM SETTING 
III. PROPOSED METHOD 
IV. EXPERIMENTAL RESULTS 
V. CONCLUSIONS 

Abstract

A large number of studies have been made on denoising of a digital noisy image. In regression filters, a convolution kernel was determined based on the spatial distance or the photometric distance. In non-local mean (NLM) filters, pixel-wise calculation of the distance was replaced with patch-wise one. Later on, NLM filters have been developed to be adaptive to the local statistics of an image with introduction of the prior knowledge in a Bayesian framework. Unlike those existing approaches, we introduce the prior knowledge, not on the local patch in NLM filters but, on the noise bias (NB) which has not been utilized so far. Although the mean of noise is assumed to be zero before tone mapping (TM), it becomes non-zero value after TM due to the non-linearity of TM. Utilizing this fact, we propose a new denoising method for a tone mapped noisy image. In this method, pixels in the noisy image are classified into several subsets according to the observed pixel value, and the pixel values in each subset are compensated based on the prior knowledge so that NB of the subset becomes close to zero. As a result of experiments, effectiveness of the proposed method is confirmed.

DOI:10.1017/ATSIP.2018.29