International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395 -0056
Volume: 04 Issue: 02 | Feb -2017
p-ISSN: 2395-0072
www.irjet.net
Survey on Various Image Denoising Techniques Sinisha George1, Silpa Joseph2 1PG
student, Dept. Of Computer Engineering, VJCET, Vazhakulam, Kerala, India
2Assistant
Professor, Dept. Of Computer Engineering, VJCET, Vazhakulam, Kerala, India
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Abstract - Nowadays digital images are playing an
important role in the area of digital image processing. The main challenging factor in image denoising is removal of noise from an image while preserving its details. Noise creates a barrier and it affects the performance by decreasing the resolution, image quality, image visuality and the object recognizing capability in images. Due to noise presence it is difficult for observer to obtain discriminate finer details and real structure of image. One of the main objectives of this survey is to analyse a detailed study in the field of Image denoising techniques. Key Words: Image Denoising, PSNR, Filtering, Noise Models
1. INTRODUCTION Any form of signal processing having image as an input & output (or a set of characteristics or parameters of image) is called image processing. In image processing we work in two domains i.e., spatial domain and frequency domain. Spatial domain refers to the digital image plane itself, and image processing method in this category are based on direct manipulation of pixels in an image and coming to frequency domain it is the analysis of mathematical signals or functions with respect to frequency rather than time. The principal sources of noise in digital images arise during image acquisition and/or transmission. It can be produced by the sensor and circuitry of a digital camera or scanner. Noise degrades the image quality for which there is a need to denoise the image to restore the quality of image. Hence, first question arises is what is noise?. Image noise means unwanted signal. It is random variation of color information and brightness in images, and is usually an aspect of electronic noises. It is an undesirable by-product of image capture that adds spurious and extraneous information. This definition includes everything about a noise. Many applications are now including the images in their methods, procedures, reports, manuals, data etc., to deal with their clients and image noise is the basic problem with these applications as it affects the data accuracy and efficiency level.
Š 2017, IRJET
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2. LITERATURE SURVEY In [1] Rizkinia, Tatsuya Baba, Student Member, Keiichiro Shirai,and Masahiro Okuda, proposed a method for local spectral component decomposition based on the line feature of local distribution. It reduce noise on multi-channel images by exploiting the linear correlation in the spectral domain of a local region. First calculate a linear feature over the spectral components of an M-channel image, which call the spectral line, and then, using the line, decompose the image into three components: a single M-channel image and two gray-scale images. By virtue of the decomposition, the noise is concentrated on the two images, and thus LSCD algorithm needs to denoise only the two grayscale images, regardless of the number of the channels. As a result, digital image deterioration due to the imbalance of the spectral component correlation can be avoided. The experiments show that LSCD improves image quality with less deterioration while preserving vivid contrast. This method is especially effective for hyper spectral images. LSCD method gives higher MPSNR results than those of the other compared methods such as VBM3D [7], PLOW[3], PRINL-PCA[4] and Bilateral[5]. In [2], Qiang Guo, Caiming Zhang, Yunfeng Zhang, and Hui Liu, proposed a Efficient SVD- Based Method for Image Denoising. This method first group’s image patches by a classification algorithm to achieve many groups of similar patches. The patch grouping step identifies similar image patches by the Euclidean distance based similarity metric. Once the similar patches are identified, and they can be estimated by the low rank approximation in the SVD-based denoising step. In the aggregation step, all processed patches are aggregated to form the denoised image. The back projection step uses the residual image to further improve the denoised result. Different from other methods such as BM3D[7] and LPGPCA[4], this method adopts the low rank approximation to estimate digital image patches and uses the back projection to avoid loss of detail information of the image. The computational complexity of this algorithm is lower than most of existing state of the art image denoising algorithms but higher than BM3D. The fixed transform used by BM3D is less complex than SVD, whereas it is less adapted to edges and textures. The main computational cost of algorithm is ISO 9001:2008 Certified Journal
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