Guideline to use these packages:

Step 1: Save 'denoise_1.0.tar.gz', 'denoisecv_1.0.tar.gz' and
        'denoiseautothresh_1.0.tar.gz' in a directory.

Step 2: Open a terminal. Go to that directory and open 'R'.

Step 3: Execute the following commands:

        install.packages("denoise_1.0.tar.gz")
        install.packages("denoisecv_1.0.tar.gz")
        install.packages("denoiseautothresh_1.0.tar.gz")

        library(denoise)
        library(denoisecv)
        library(denoiseautothresh)

Step 4: Now, the R-commands, 'denoise', 'denoisecv' and
        'denoiseautothresh' can be used. Helps are also
        available.

Brief description of what these programs do:
More detailed description can be found in 'help' of R programs.

denoise: It denoises 3-D images. It also calculates estimated MISE,
         JS when true image is known. Here, the user can specify
         all the three parameters h_star, u and h.

denoiseautothresh: It denoises 3-D images. It also calculates
         estimated MISE, JS when true image is known. Here,
         the u is chosen as default assuming alpha = 0.001.

denoisecv: It calculates CV-score. This program can be used to
         select the parameters h_star and h.

One simple example:
Corresponds to Method = New, n=64, sigma=0.1, Table=1

      n1 <- 64
      n2 <- 64
      n3 <- 64
      z <- rep(0,n1*n2*n3)
      for (i in 1:n1){ for (j in 1:n2){ for (k in 1:n3){
      z[(i-1)*n2*n3+(j-1)*n3+k] <- -(i/n1-0.5)^2 -(j/n2-0.5)^2 -(k/n3-0.5)^2
      if (  ((abs(i/n1-0.5)<=0.25) & (abs(j/n2-0.5)<=0.25) & (abs(k/n3-0.5)<=0.25)) )
      {z[(i-1)*n2*n3+(j-1)*n3+k] <- -(i/n1-0.5)^2 -(j/n2-0.5)^2 -(k/n3-0.5)^2 + 1}
      if ( (((i/n1-0.5)^2+(j/n2-0.5)^2 <=0.15^2) & (abs(k/n3-0.5)<=0.35))  )
      {z[(i-1)*n2*n3+(j-1)*n3+k] <- -(i/n1-0.5)^2 -(j/n2-0.5)^2 -(k/n3-0.5)^2 + 1}
      }}}
     
      ntemp <- n1*n2*n3
      g <- rep(0,ntemp)
      for (i in 1:ntemp){
      g[i] <- z[i] + rnorm(1,0,0.1)
      }
     
      result <- denoise(z,g,64,64,64,0.023,13,0.037)
      result$out_mse
      result$out_js
      result$out_js_true
      ep <- (result$out_js_true - result$out_js)/result$out_js_true
      ep
      t <- array(0,dim=c(n1,n2,n3))
      for (i in 1:n1) {for (j in 1:n2){ for (k in 1:n3){
      t[i,j,k] <- result$output[(i-1)*n2*n3+(j-1)*n3+k]}}}
      image(c(1:64),c(1:64),t[32,1:64,1:64],col=gray((0:128)/128))