Digital images are generally affected by noise due to acquisition process or by transmission process. Most of the images are assumed to have variety of noise. Different algorithms are used depending on the noise model. Number of algorithms have been developed in the past years to denoise the images. In this paper different existing denoising algorithms for impulse noise are studied and compared. Almost all algorithms studied here are executed in two steps, first step is to detect the corrupted pixels and second step is to correct the pixels by replacing the filter estimated values. The performances of the algorithms are compared based on PSNR values. Some algorithms like median filter work better at low density level and the algorithms like RWMF are good at high density levels with changing window size.
Remote sensing images (ranges from satellite to seismic) are affected by number of noises like interference, impulse and speckle noises. Image denoising is one of the traditional problems in digital image processing, which plays vital role as a pre-processing step in number of image and video applications. Image denoising still remains a challenging research area for researchers because noise removal introduces artifacts and causes blurring of the images. This study is done with the intension of designing a best algorithm for impulsive noise reduction in an industrial environment. A review of the typical impulsive noise reduction systems which are based on order statistics are done and particularized for the described situation. Finally, computational aspects are analyzed in terms of PSNR values and some solutions are proposed.
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