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.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
This paper uses robust statistics and curvelet transform to learn a general-purpose no-reference (NR) image quality assessment (IQA) model. The new approach, here called M1, competes with the Curvelet Quality Assessment proposed in 2014 (Curvelet2014). The central idea is to use descriptors based on robust statistics to extract features and predict the human opinion about degraded images. To show the consistency of the method the model is tested with 3 different datasets, LIVE IQA, TID2013 and CSIQ. To test evaluation, it is used the Wilcoxon test to verify the statistical significance of results and promote an accurate comparison between new model M1 and Curvelet2014. The results show a gain when robust statistics are used as descriptor.
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