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Image denoising is one of the widely studied unsolved problems and plays a significant role in the research area of image processing and computer vision. Most of the time images are contaminated by impulse noise during the process of image acquisition or at the time of transmission due to malfunctioning image pixels in the camera sensors, channel transmission errors or faulty storage hardware. Therefore, a pre-processing stage is always required before processing an image for any application. Noise filtering is one of the important parts of this stage. The objectives of image denoising algorithms are to detect and suppress the unhealthy pixel elements in the test image without harming the fine details of the image. Impulse noise found in digital images is a spark that disturbs the information contain in the images. It distorts the pixels of a digital image by replacing the original value either by fixed value or any random value within the available dynamic range. So there are two categories of impulse noise as per the distribution of noise in an image: salt and pepper noise (SPN) and Random valued impulse noise (RVIN) (Dey, Ashour, Beagum et al., 2015) (Ikeda, Gupta, Dey et al., 2015). Impulse noises can be mathematically described by the following model (Patel, Jena, Majhi & Tripathy, 2012):
where,
specifies a certain location of a pixel in the test noisy image,
indicates an original healthy image pixel element and
represents a location where a pixel element is contaminated by impulse noise. In salt-and-pepper noise(SPN), the corrupted pixels either obtain the minimum value or the maximum value of the available dynamic range i.e.
, and in random-valued impulse noise(RVIN), the corrupted pixel element can attain any possible value of the available dynamic range i.e.
where,
denote the minimum and the maximum pixel intensity within the available dynamic range of the image respectively. Therefore, the detection and suppression of presence of RVIN in an image is a challenging work (Gonzalez & Wood, 2002; Chanda & Majumder, 2002; Kang & Wang, 2009). Figure 1 shows the result of image corrupted by RVIN and SPN noise. Most of the existing algorithm performs very well for suppression of salt and pepper noise (SPN), whereas the performance towards random valued impulse noise (RVIN) is quite miserable (Jena, Patel &Majhi, 2014).