Impulse Noise Removal Using Adaptive Radial Basis Function Interpolation

Adaptive switching interpolation filter for restoring impulse corrupted digital images

“…Similar to the comparative algorithms used in the study, the traditional noise model of salt and pepper impulses that corrupts random positions with extreme values in dynamic range is only considered by the proposed algorithm. As other algorithms in the literature [49–53], the noise identification stage of the filter detects the noisy positions by comparing each pixel with the extreme possible grey values in the dynamic range. The corrupted pixels are restored by interpolating natural neighbours based on the percentage of overlapping and distance between the sites created by noisy pixels with the sites of uncorrupted pixels in the Voronoi tessellation.…”

“…Step 1 : As other algorithms [49–53], the pixels of are declared corrupted if the corresponding value of the pixel is either 0 or 255. However, if the pixel takes value other than 0 or 255 in an 8‐bit image, these positions are declared uncorrupted.…”

“…Since interpolation shows a better approximation of missing amplitude surface points that are fairly close to the original value, the restoration techniques based on interpolation show better accuracy and smoothness than other methods [48]. Interpolation approaches are widely used in the areas of meteorology for weather forecasting, geology for modelling layer structures, cartography for drawing contour maps and isolines, reducing computation time (CT) of complex functions, modelling environmental data, spatial up‐sampling, curve and surface fitting, terrain modelling, hydrology, mineral prospecting, and hydrocarbon explorations [49–54]. Scattered data interpolation techniques approximate functions using sample data from unevenly scattered spatial locations.…”

An iterative method for impulse noise removal

“…Subsequently, Nikolova 4 proposed a variational method for impulse noise removal, which can preserve details and the edges well, but the grey level of every pixel is changed including uncorrupted ones. Other types of methods have also been extensively studied [5][6][7][8][9][10][11] .…”

A Robust Functional Minimization Technique to Protect Image Details from Disturbances

“…However, the image smoothing problems significantly come into the notice are reported in many research articles including local filtering-based image smoothing [1,2], nonlocal-based methods [3][4][5][6] are also noted in literature. Nonlocal wavelet-based method [7,8], nonlocal-based sparse coding strategy [9], nonlocal lowrank [10], the sparse representation techniques [11], shearlet-based model [12], curvelet-based method [13], dictionary-based approaches [14,15], soft-thresholding method [16], image deblurring technique using regularization [17], the radial basis function (RBF)-based method [18] and image retrieval with color and angle representation [19] are also remarkable in applications. However, many other methods based on variation and partial differential equation (PDE) have been proposed widely since variational calculus come out recently as a powerful tool for image-smoothing and model solutions.…”