Abstract
Abstract In the data collection phase, the digital images are captured using sensors that often contaminated by noise (undesired random signal). In digital image processing task, enhancing the image quality and reducing the noise is a central process. Image denoising effectively preserves the image edges to a higher extend in the flat regions. Several adaptive filters (median filter, Gaussian filter, fuzzy filter, etc.) have been utilized to improve the smoothness of digital image, but these filters failed to preserve the image edges while removing noise. In this paper, a modified fuzzy set filter has been proposed to eliminate noise for restoring the digital image. Usually in fuzzy set filter, sixteen fuzzy rules are generated to find the noisy pixels in the digital image. In modified fuzzy set filter, a set of twenty-four fuzzy rules are generated with additional four pixel locations for determining the noisy pixels in the digital image. The additional eight fuzzy rules ease the process of finding the image pixels,whether it required averaging or not. In this scenario, the input digital images were collected from the underwater photography fish dataset. The efficiency of the modified fuzzy set filter was evaluated by varying degrees of Gaussian noise (0.01, 0.03, and 0.1 levels of Gaussian noise). For performance evaluation, Structural Similarity (SSIM), Mean Structural Similarity (MSSIM), Mean Square Error (MSE), Normalized Mean Square Error (NMSE), Universal Image Quality Index (UIQI), Peak Signal to Noise Ratio (PSNR), and Visual Information Fidelity (VIF) were used. The experimental results showed that the modified fuzzy set filter improved PSNR value up to 2-3 dB, MSSIM up to 0.12-0.03, and NMSE value up to 0.38-0.1 compared to the traditional filtering techniques.
Highlights
In recent decades, the digital images are playing an important role in numerous applications like computer vision, medical imaging, biometrics, etc. and in the field of engineering science: geographical systems and astronomy [1, 2]
The noisy digital images are harnessed to the intended operations, when the noise level is too high
Many image denoising schemes are developed in several applications like medical image analysis, biometric authentication, pattern recognition etc. for reducing the noise level in a digital image
Summary
The digital images are playing an important role in numerous applications like computer vision, medical imaging, biometrics, etc. and in the field of engineering science: geographical systems and astronomy [1, 2]. Due to intrinsic thermal fluctuations, imperfect device data collection and transmission, imperfection of lens device and external interface, noise is introduced inevitably in the captured digital images [3, 4]. Image denoising is a key procedure for restoring the noiseless image from the noisy observations that helps in preserving the edges and textures present in the digital images [5, 6]. Image denoising is considered as a necessary step in texture analysis, feature extraction and segmentation [7]. There are many denoising methodologies available for eliminating noise from the digital images. The conventional denoising algorithms have been developed by considering the parameters like noise and artifacts. The existing denoising methods are categorized into two types such as nonlocal self-similarity based methods and conventional
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