An efficient way to use MS‐CLEAN associated with Shannon's entropy

Abstract

Abstract This article covers deconvolution methods in the context of radio astronomical images. A new formulation is proposed to deal with negative brightness, deconvoluting separately the positive and negative brightness of the sky. The positive brightness is physically possible, but negative brightness is a degradation product. At the same time, the paper presents Shannon's entropy's behaviour in the context of the Multi‐Scale CLEAN (MS‐CLEAN) algorithm, defining the measured brightness as information in the scope of Shannon's entropy. The knowledge acquired is used in an example of information monitoring at scales, which automatically reduces the search space of MS‐CLEAN, and reduces the computational cost. The proposed algorithm, called Relevant Component Multi‐Scale CLEAN (RC‐CLEAN), can be up to 4 times faster than the classic MS‐CLEAN without prejudice to the identification of structures and noise reduction. Here, Structural Similarity Index (SSIM) and Peak Signal to Noise Ratio (PSNR) used to quantify the results, respectively, showed the same quality for the SSIM and gains of up to 11 dB for the PSNR. RC‐CLEAN also shows a result similar to that obtained by the standard software of large astronomical laboratories using real data.

Keywords

• Thermodynamic functions and equations of state
• Image processing and restoration
• Radiotelescopes
• Astronomical data and image processing
• Mathematical and computer techniques in astronomy