Denoising of Uncertain Type Noise Images by Spatial Feature Classification in Nonsubsampled Shearlet Transform

Zhiyu Lyu,Min Han,Decai Li

doi:10.1109/access.2019.2962744

Abstract

Most denoising methods are designed to deal standard images with specific type noise, which do not perform well when denoising real noisy images contain uncertain types of noise. However, underwater image is a typical uncertain type noise image. To solve this problem, this paper presents a method using spatial feature classification jointing nonsubsampled shearlet transform (NSST) for denoising uncertain type noise images. Justifiable granule is employed to solve the problem of parameter selection. The raw image was decomposed by using the NSST to get one low frequency subband and several high frequency subbands. Then, the preliminary binary map is built, the binary map is employed to decide whether a coefficient contains spatial feature or not. And we employ justifiable granule to solve the difficulty of parameter selection. The high subbands coefficients are classified into two classes by fuzzy support vector machine classification: the texture class and the noise class. At last, the adaptive Bayesian threshold is used to shrink the coefficients. Simulation results show the proposed method is effective in uncertain type noise images(also have good performance in specific type noise). The method we proposed has been compared with other popular denoising methods and get excellent subjective performance and PSNR improvement.

Highlights

The corruption of images is common during its acquisition, processing, compression and transmission
2) We build a binary map and employ the binary map to decide whether a coefficient contains spatial feature or not in nonsubsampled shearlet transform domain
In this paper, we proposed an uncertain noise images denoising method by using fuzzy Support vector machine (SVM) classification jointing with the nonsubsampled shearlet transform (NSST)

Summary

INTRODUCTION

The corruption of images is common during its acquisition, processing, compression and transmission. We use shearlet transform to denoising the uncertain type noise image.We briefly introduce the Transform Based Methods as follows: 1) WAVELET PACKET TRANSFORM The Wavelet packet transform is based on wavelet transform and its further generalized. It can decompose the high-frequency of the wavelet without subdivision, and adaptively select the corresponding frequency band. The fuzzy SVM with the membership function described above can acquire satisfiable performance because it use average algorithm to deal with training samples. The influence of a single sample in the training set is very little so that the effect of outliers can be removed

THE DENOISING METHOD BY FUZZY SVM WITH NSST

SIMULATION RESULTS

CONCLUSION