Installation

For installing imagine, as follows:

install.packages("imagine")

Engines

imagine performs algorithms written in Rcpp (called ‘engines’), ensuring a faster application of filters. At version 1.5.0, imagine includes four main engines, described as follows:

• Engine 1: Basic 2D-convolution algorithm. It multiplies kernel by the neighborhood of each cell and then it sums all values.
• Engine 2: Make the same of Engine 1 but, at the end, it returns the position indicated by the parameter probs.
• Engine 3: It uses the radius argument to extract the values of squared neighborhood (\(radius \times radius\)) and calculates the mean.
• Engine 4: It uses the radius argument to extract the values of squared neighborhood (\(radius \times radius\)) and returns the position indicated by the parameter probs
• Engine 5: It performs a generalization of the Contextual Median Filter propose by Belkin & O’Reilly (2009). The default parameters reproduce the algorithm of the paper, but the function allows certain customization.

Main functions

There are 5 main functions and 2 wrappers:

Convolution functions

# Build kernels
# Kernel 1: For bottom edge recognition
kernel1 <- matrix(c(-1, -2, -1,
                     0,  0,  0,
                     1,  2,  1), 
                  nrow = 3)

# Kernel 2: Diagonal weighting
kernel2 <- matrix(c(-2, 0, 0,
                     0, 1, 0,
                     0, 0, 2), 
                  nrow = 3)

# Apply filters
convolutionExample  <- convolution2D(X = wbImage, kernel = kernel1)
convQuantileExample <- convolutionQuantile(X = wbImage, kernel = kernel2, probs = 0.1)

In order to compare results, we will plot both data (original and filtered) using image function, as shows in figures 1 and 2.

Original

Figure 2: Original matrix

Filtered

Figure 1: Filtered matrices

Median-filter asociated functions

# Add some noise (NA) to the image (matrix)
set.seed(7)
naIndex <- sample(x = seq(prod(dim(myMatrix))), size = as.integer(0.4*prod(dim(myMatrix))), replace = FALSE)
myMatrix[naIndex] <- NA

# Build kernel
radius <- 3

# Apply filters
meanfilterExample     <- meanFilter(X = myMatrix, radius = radius)
quantilefilterExample <- quantileFilter(X = myMatrix, radius = radius, probs = 0.1)
medianfilterExample   <- medianFilter(X = myMatrix, radius = radius, times = 10)

Now, we will plot both data (original and filtered) using image function, as shows in figures 1 and 2.

Original

Figure 2: Original matrix

Filtered

Figure 1: Filtered matrices

Kernel application

In the field of image processing, one of the tools most commonly used are the convolutions, which consist of operations between two arrays: The array of image data (as a big matrix) and kernels (as small matrices) which weighs each pixel values by the values of its corresponding neighborhood. Different kernels produce different effects, for instance: blur, shifted images (right, left, up or down), sharpening, etc. The users must be cautious with the size of the kernel because the larger the radius, the more pixels remain unanalyzed at the edges.