Noise bias compensation for tone mapped noisy image using prior knowledge

Hitoshi Kiya,Masahiro Iwahashi,Sayaka Minewaki,Taichi Yoshida,Yoshinori Takei

doi:10.1017/atsip.2018.29

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.

Highlights

A large number of studies have been made on denoising, most of them are focused on utilizing correlation between pixels
NB compensation (NBC) in this paper classifies pixels in the noisy image into several subsets according to the observed pixel value, and compensates the pixel value in each subset with a preliminarily determined compensation value
Denoising performance is improved by combination. This means that NBC can coexist with approaches focusing on the correlation between pixels like nonlocal mean (NLM) filter

Summary

INTRODUCTION

A large number of studies have been made on denoising, most of them are focused on utilizing correlation between pixels. NB in this paper is the mean of the noise in the subset corresponding to the observation pixel value and it is compensated. A method of determining the compensation value from all pixel values in an input image based on the Bayesian inference theory was reported without enough experimental results [30]. We propose a new method based on compensation value calculated from reduced information of the histogram of an input image and the noise before TM. It is assumed that the histogram of the pixel values in an input image is included in the overhead information which is reduced much more than [30].

PROBLEM SETTING

PROPOSED METHOD

EXPERIMENTAL RESULTS

CONCLUSIONS