Improved Image Enhancement Algorithms based on the Switching Median Filtering Technique
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RESEARCH ARTICLE-COMPUTER ENGINEERING AND COMPUTER SCIENCE
Improved Image Enhancement Algorithms based on the Switching Median Filtering Technique Shamama Anwar1
· G. Rajamohan2
Received: 29 November 2019 / Accepted: 23 September 2020 © King Fahd University of Petroleum & Minerals 2020
Abstract Noisy or corrupt images are characterized by poor contrast and ill-defined ridges and valleys. Such images need to be enhanced using suitable image enhancement techniques so that they can be used with other applications. Two improved image enhancement algorithms based on the switching median filtering are proposed in the present work. Images simulated with varying levels of Gaussian and salt-and-pepper noises are used for studying the efficacy of the proposed algorithms. A comparison with existing switching median filtering algorithm, in terms of visual quality of images, peak-signal-to-noise (PSNR) ratio and structural similarity index (SSIM), reveals the superiority of the proposed algorithms. Keywords Gaussian noise · Image enhancement techniques · Peak-signal-to-noise ratio · Salt-and-pepper noise · Structural similarity index · Switching median filtering
1 Introduction Images containing noise are called corrupt or noisy images. Noise in images refer to the random variations in their brightness and color information. Noisy images will be visually displeasing as they may not reveal all the details of corresponding original scenes. This makes the removal of noise from such images essential, prior to any subsequent processing. Image enhancement techniques play a key role in removing the noise from noisy images and generating visually pleasing and more informative images. Such techniques may be grouped into spatial and frequency domain methods. Spatial domain methods operate directly on individual pixels (intensity transformations) or group of pixels (spatial filtering). Frequency domain methods, on the other hand, operate on the Fourier transforms of the images [1]. An inverse Fourier transform is performed to get the enhanced image [2].
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Shamama Anwar [email protected]
1
Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, 835215 Ranchi, India
2
Department of Manufacturing Engineering, National Institute of Foundry and Forge Technology, Hatia, 834003 Ranchi, India
Histogram manipulation forms the basis for numerous image enhancement techniques in the spatial domain. The histogram processing methods operate on individual pixels of noisy images to enhance them by performing intensity transformations. As the present work focuses on spatial filtering, the interested reader is directed elsewhere [3–7] for some recent research related to histogram processing. The spatial filtering techniques initially use linear filters that operate on a group of pixels, selected using a window of specified size. Severe blurring in images filtered by linear filters led to the development of nonlinear filters. A median filter is the most widely used nonlinear filter [8]. The simplicity in impleme
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