A Spatial Median Filter for Noise Removal in Digital Images
A Spatial Median Filter for Noise Removal in Digital Images
James C. Church, Yixin Chen, and Stephen V. Rice Department of Computer and Information Science, University of Mississippi {jcchurch,ychen,rice}@cs.olemiss.edu
Abstract
In this paper, six different image filtering algorithms are compared based on their ability to reconstruct noiseaffected images. The purpose of these algorithms is to remove noise from a signal that might occur through the transmission of an image. A new algorithm, the Spatial Median Filter, is introduced and compared with current image smoothing techniques. Experimental results demonstrate that the proposed algorithm is comparable to these techniques. A modification to this algorithm is introduced to achieve more accurate reconstructions over other popular techniques.
Figure 1 demonstrates five common filtering algorithms applied to an original image.
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1. Smoothing Algorithms
The inexpensiveness and simplicity of point-andshoot cameras, combined with the speed at which budding photographers can send their photos over the Internet to be viewed by the world, makes digital photography a popular hobby. With each snap of a digital photograph, a signal is transmitted from a photon sensor to a memory chip embedded inside a camera. Transmission technology is prone to a degree of error, and noise is added to each photograph. Significant work has been done in both hardware and software to improve the signal-to-noise ratio in digital photography. In software, a smoothing filter is used to remove noise from an image. Each pixel is represented by three scalar values representing the red, green, and blue chromatic intensities. At each pixel studied, a smoothing filter takes into account the surrounding pixels to derive a more accurate version of this pixel. By taking neighboring pixels into consideration, extreme “noisy” pixels can be replaced. However, outlier pixels may represent uncorrupted fine details,
James C. Church, Yixin Chen, and Stephen V. Rice Department of Computer and Information Science, University of Mississippi {jcchurch,ychen,rice}@cs.olemiss.edu
Abstract
In this paper, six different image filtering algorithms are compared based on their ability to reconstruct noiseaffected images. The purpose of these algorithms is to remove noise from a signal that might occur through the transmission of an image. A new algorithm, the Spatial Median Filter, is introduced and compared with current image smoothing techniques. Experimental results demonstrate that the proposed algorithm is comparable to these techniques. A modification to this algorithm is introduced to achieve more accurate reconstructions over other popular techniques.
Figure 1 demonstrates five common filtering algorithms applied to an original image.
(a)
(b)
(c)
(d)
(e)
(f)
1. Smoothing Algorithms
The inexpensiveness and simplicity of point-andshoot cameras, combined with the speed at which budding photographers can send their photos over the Internet to be viewed by the world, makes digital photography a popular hobby. With each snap of a digital photograph, a signal is transmitted from a photon sensor to a memory chip embedded inside a camera. Transmission technology is prone to a degree of error, and noise is added to each photograph. Significant work has been done in both hardware and software to improve the signal-to-noise ratio in digital photography. In software, a smoothing filter is used to remove noise from an image. Each pixel is represented by three scalar values representing the red, green, and blue chromatic intensities. At each pixel studied, a smoothing filter takes into account the surrounding pixels to derive a more accurate version of this pixel. By taking neighboring pixels into consideration, extreme “noisy” pixels can be replaced. However, outlier pixels may represent uncorrupted fine details,
References: [1] J. W. Tukey, (1974). Nonlinear (Nonsuperposable) Methods for Smoothing Data. Conference Record EASCON, p. 673. [2] N. C. Gallagher, Jr. and G. L. Wise, (1981). A The- 623