What's more, we also use the image entropy, contrast, and peak value to evaluate the focused images in Figure 7, which are presented in Tables 1, 2, and 3, respectively.
Inspired by this, a novel scheme is established by using the 2D image entropy as the penalty function to jointly optimize the range shift and phase error.
Since the entropy of an image is usually used as an index that quantitatively represents the information of the luminosity distribution of the image, entropy criterion is used to analyze the performance for the complete aperture case.
Noisy pixels are detected based on iterative measuring the image entropy and restored using an Adaptive Iterative Mean filter (AIM).
However, owing to the use of parametric superresolution approaches, the image entropy criterion is not suitable to SA-ISAR images [4].
In general, the image entropy function with respect to α ˜ i k is usually smooth, and we can use only a number of discrete samples to recover the entire entropy function by interpolation.
The anisotropy measure is computed using the Renyi entropy on DCT image patches along four different orientations θ=0,45,90,135 in degrees.
In [4, 9], different methods were proposed for computing the motion and image complexities, for instance, using the spectral entropy, and other metrics.
Figure 5 illustrates the recovered images at the different stages during the recovery process using the information entropy theory described in the last section, where the entropy curve as a function of the diffraction distance z in the entire process is shown in the middle of the figure.
Also, we suggest a fractional functional entropy formula by using the Tsallis entropy.
