Image Denoising Using SWT 2D Wavelet Transform
IJSTE - International Journal of Science Technology & Engineering Volume 3 Issue 01 July 2016ISSN (online): 2349-784XImage Denoising using SWT 2D WaveletTransformDeep Singh BediDepartment of Electronics & Communication EngineeringGNDU AmritsarPawandeep KaurDepartment of Electronics & Communication EngineeringGNDU AmritsarAbstractImages undergo deterioration in quality when processed by some means. The Signal strength drops when it undergoes a series ofoperations. The main deciding factor for this degradation therefore depends on the signal to noise ratio of the processed imagewith respect to the unprocessed image that is how much loss in the quality of the processed image or how much intervention ofnoise occurs during processing. Using the SWT 2D denoising technique we compensate for such noise distortions usingMATLAB as a platform. The proposed algorithm exhibits promising results from quantitatively and qualitatively points of view.Keywords: Discrete wavelet transform, Image denoising, Wavelet transform, Peak signal to noise ratio, Sea levelAbbreviations: SLR: sea level rise, PSNR: peak signal to noise ratio, DWT: discrete wavelets transform, SWT: stationarywavelet transformI.INTRODUCTIONImage processing field is a huge field to deal with. It encompasses in the following areas: 1. Image Compression, 2. Image Denoising, 3. Image Enhancement, 4. Image Recognition, 5. Feature Detection, and 6. Texture Classification [1]. Wavelet-basedtechniques apply to all of these topics.An image is often corrupted by noise in its acquisition and transmission or during itsprocessing. Image denoising is used to remove the additive noise introduced during processing while retaining as much aspossible the important signal features. In the recent years a lot of research on wavelet thresholding and threshold selection forsignal de-noising [2], [3]-[11] has been done, because wavelet provides an appropriate basis for separating noisy signal from theoriginal image signal which contains the data. This is mainly because wavelet transform is good at energy compaction, the smallcoefficients are more likely due to noise and large coefficients due to important signal features [8]. These small coefficients canbe thresholded without affecting the significant features of the image. Thresholding is a simple non-linear technique, whichoperates on one wavelet coefficient at a time. Each coefficient is compared against the threshold chosen, if the coefficient issmaller than threshold value it is set to zero value otherwise it is kept or modified. Replacing the small noisy coefficients by zeroand taking inverse wavelet transform on the result may lead to the reconstruction with the essential signal characteristics retainedand with lesser noise.In the proposed algorithm the image used is the binary conversion of a satellite image that is used tocalculate the SLR(Sea level rise)[22]. Since the processing is done in MATLAB as a platform, the processing is quite accurateand very low noise or distortion is present.in the processed binary image. The noise in a binary image can take only the form ofadding a ‘1’ in place of ‘0’ or vice versa. If very accurate results are desired which is the case for SLR, image denoising isrequired to compensate even a little loss of data. Image denoising using SWT 2D wavelet transform is used for denoising thebinary part, the PSNR (Peak signal to noise ratio) is calculated for the initial grayscale to binary image and the grayscale to thefinal denoised image.II. LITERATURE REVIEWThere has been a lot of research on the way of defining and assigning the threshold levels and their type (i.e. hard or softthreshold) after the work of Donoho and Johnstone [2], [4], [9], [10]. Matlab wavelet toolbox includes functions and techniquesfor 1-D, 2-D and 3D de-noising [12], which are based on Donoho’s algorithm. Nevertheless, in the 2-D case, there is no optionfor the selection
Denoised image 3 576.8 576.8 422.4 422.4 422.4 4.7222 V. CONCLUSION In this paper effective denoising technique is applied using SWT 2D denoising in MATLAB. The processed image during image processing [22] causes intervention of noise and cause signal degradation and to compensate for the loss of quality of the image
one for image denoising. In the course of the project, we also aimed to use wavelet denoising as a means of compression and were successfully able to implement a compression technique based on a unified denoising and compression principle. 1.2 The concept of denoising A more precise explanation of the wavelet denoising procedure can be given .
2.2 Image Denoising. A typical application area for image reconstruction is image denoising, where the task is to remove noise to restore the original image. Here, we focus on image denoising tech-niques based on deep neural networks; for more detailed information about image denoising research, please refer to the following survey papers [9,11].
In the recent years there has been a fair amount of research on wavelet based image denoising, because wavelet provides an appropriate basis for image denoising. But this single tree wavelet based image denoising has poor directionality, loss of phase information and shift sensitivity [11] as
4 Image Denoising In image processing, wavelets are used for instance for edges detection, watermarking, texture detection, compression, denoising, and coding of interesting features for subsequent classifica-tion [2]. Image denoising by thresholding of the DWT coefficients is discussed in the following subsections. 4.1 Principles
age denoising based on minimization of total variation (TV) has gained certain popularity in the literature (e.g., [4]), and the TV approach is initially suggested for denoising 2-D images (e.g. [12]). MATLAB pro-grams for 3-D image denoising using anisotropic dif-fusion have also been developed (e.g., [6]). Other
Image denoising and inpainting are common image restoration problems that are both useful by themselves and important preprocessing steps of many other applications. Image denoising problems arise when an image is corrupted by additive white Gaussian
Image denoising can be classified into (1) pixel domain and (2) frequency domain. In pixel domain, each pixel in the noisy image is modified with a view of removing noise from the image. As most of the noises are in the high frequency domain, the denoising algorithm generally implement some kind of low pass filtering. Gaussian
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