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Improvement of image denoising with interpolation and RAISR (本文)

Theingi Zin 慶應義塾大学

2021.05.26

概要

Digital images are frequently contaminated by noise due to different sources such as transmission errors, malfunctioning pixel elements in the camera sensors, faulty memory location and timing errors in analog-to-digital conversion. The presence of noise in digital images leads to some undesirable effects such as image degradation and distortion of some important image features. Therefore, image denoising has recently become essential in many subsequent image processing applications as a pre- processing step. The aim of image denoising is to efficiently attenuate the corrupted noise and preserve the image details such as edges and textures in the image. Some- times, digital images are degraded by single noise as well as more than one type of noise. Denoising methods may be different depending upon the types of noise because the characteristics of noise and filtering approaches are dissimilar to each other. The objectives of this thesis are to enhance the quantitative performance of mixed-noise removal method with interpolation approach while preserving the image details and improve the denoising performance of Gaussian noise removal method via Improved Rapid and Accurate Image Super-Resolution (IRAISR) with the reduced number of filters without sacrificing salient image features.

The suppression of mixed-noise composed of Additive White Gaussian Noise (AWGN) and Random-Valued Impulse Noise (RVIN) is considered in this thesis. There are mainly two steps in the removal of mixed noise. The first step is to achieve the denoised image by integrating Interpolation, Directional Weighted Me- dian (DWM) filter, downsampling and Block Matching and 3D (BM3D) filtering. The second step is to obtain the restored image by combining re-detect process which is thresholding on the absolute difference between the input noisy image and the pre-estimated image from the first step, and BM3D. Even though some conventional mixed-noise removal methods can successfully filter the noise, some image details are lost due to the miss detection of the image details as the impulse noise. In order to overcome this issue, the interpolation technique based on multi-surface fitting for single image is added before the detection of impulse noise in DWM filter. And then, it is also necessary to down-sample the interpolated DWM output because of the effect of interpolation. As most mixed-noise removal methods are detection-based, the detection of impulse noise in eliminating the mixed-noise plays a vital role to be considered. The addition of interpolation before DWM filter is very efficient in detecting the impulse noise to achieve an excellent denoising performance.

On the other hand, the elimination of Gaussian noise can be improved by em- ploying IRAISR as a post-processing step to prevent from distortion of some image structures because of the deterioration of high frequency components in the existing noise removal methods. In this method, the two steps are basically structured namely: learning phase and testing phase. The filters are learned from the image pairs be- tween the patches extracted from the images denoised by nonlocal-based benchmark methods such as BM3D and Weighted Nuclear Norm Minimization (WNNM), and the pixels from Ground truth by eigen-analysis in the learning phase. The filtered image can be obtained by applying the pre-learned filters which are the reduction to 18 filters in the hash classes by two improvements including geometric conversion for the gradient angle and the minimization of the classes for the gradient strength to the patches extracted from the denoised image in the testing phase. Moreover, the Census transform (CT) is also utilized by blending the image attenuated by Gaussian noise removal techniques and the filtered output to restore the local structures of the image within a wide range of frequencies.

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