Orthogonal Compactly Supported Near-Symmetric Wavelets in Denoising Satellite Images

Tilak, T. N.; Krishnakumar, S.

doi:10.17485/ijst/2017/v10i14/111965

Cited by 2 publications

(1 citation statement)

References 16 publications

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“…It is essential to eliminate AWGN noise from the noisy image till it approximates the input image. Therefore, noise is one of the major sources of information in several applications and one of the significant constraints in image accuracy [3]. Denoising an image seeks to eliminate or reduce the amount of noise and attempt to retrieve valuable information from degraded and blurred images.…”

Section: Introductionmentioning

confidence: 99%

Image Denoising Based on Implementing Threshold Techniques in Multi-Resolution Wavelet Domain and Spatial Domain Filters

Ismael¹,

Baykara²

2022

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Nowadays, a digital image is often easily corrupted due to different forms of noise and complex processes resulting from the acquisition, compression, encoding, transportation, storage, retrieval, etc. All of these factors cause image quality to be distorted and visual information to be lost; in order to overcome this problem, Image denoising techniques are used widely to eliminate the various forms of noise that exist in the deteriorating image while keeping as many fine details and vital signal features as possible in the digital image. The wavelet denoising method aims to remove unwanted noise from a noisy image while preserving its vital features as a result of its ability to divide the degraded image into four sub-bands (sub-images) and operate at the frequencies of each one separately, where acquiring the original image content is vital to achieving reliable performance. This work introduces and implements a new hybrid system to the image denoising caused by Additive White Gaussian Noise (AWGN). The hybrid system is achieved using a combination of Median and Wiener filters as spatial domain filters with two-dimensional stationary and discrete wavelet transform (2D-SWT, 2D-DWT) as a multi-resolution analysis technique by applying 131 wfilters from the wavelet families (haar, db, sym, coif, bior, rbio, dmey, fk) in image processing at three levels of decomposition based on Hard, SureShrink, Bayesian, and Penalized threshold techniques on both high and low frequencies to distinguish and remove noise from affected pixel units and obtain improved results of the noise reduction process to the noisy image. Then, the multi-level 2D inverse wavelet transform (2D-IWT) eliminates noise and completes the image reconstruction by the hybrid denoising technique. Finally, the performance of the hybrid system has been estimated and measured by the peak signal-to-noise ratio (PSNR) value as an image quality metric. Experimental evaluation findings that the results of the proposed approach improved by about 17.5% by comparing them to the results of the related work, as well as enhancement the essence of image quality in terms of better noise reduction and edge preservation instead of using a multi-resolution WT domain or spatial domain filters separately.

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Section: Introductionmentioning

confidence: 99%

Image Denoising Based on Implementing Threshold Techniques in Multi-Resolution Wavelet Domain and Spatial Domain Filters

Ismael¹,

Baykara²

2022

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Adaptive Filter with Type-2 Fuzzy System and Optimization-Based Kernel Interpolation for Satellite Image Denoising

Mahalakshmi¹,

Sreenivas

Tzovaras³

2020

The Computer Journal

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Satellite image denoising is a recent trend in image processing, but faces many challenges due to the environmental factors. Previous works have developed many filters for denoising the hyperspectral satellite images. Accordingly, this work utilizes an adaptive filter with the type 2 fuzzy system and the optimization-based kernel interpolation for the satellite image denoising. Here, the image denoising has been done through three steps, namely noise identification, noise correction and image enhancement. Initially, the type 2 fuzzy system identifies the noisy pixels in the satellite image and converts the image into a binary image, which is passed through the adaptive nonlocal mean filter (ANLMF) for the noise correction. Finally, the kernel-based interpolation scheme carries out the image enhancement, which is done through the proposed chronological Jaya optimization algorithm (chronological JOA) that is developed by modifying Jaya optimization algorithm (JOA) with the chronological idea. The performance of the proposed denoising scheme is analyzed by considering the satellite images from two standard databases, namely Indian pines database and NRSC/ISRO satellite database. Also, the comparative analysis is performed with the state-of-the-art denoising methods using the evaluation metrics, peak signal to noise ratio (PSNR), structural similarity index (SSIM) and second derivative-like measure of enhancement (SDME). From the results, it is exposed that the proposed adaptive filter with the chronological JOA has the improved performance with the PSNR of 22.0408 dB, SDME of 244.133 dB and SSIM of 0.872.

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Orthogonal Compactly Supported Near-Symmetric Wavelets in Denoising Satellite Images

Cited by 2 publications

References 16 publications

Image Denoising Based on Implementing Threshold Techniques in Multi-Resolution Wavelet Domain and Spatial Domain Filters

Image Denoising Based on Implementing Threshold Techniques in Multi-Resolution Wavelet Domain and Spatial Domain Filters

Adaptive Filter with Type-2 Fuzzy System and Optimization-Based Kernel Interpolation for Satellite Image Denoising

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