Abstract
There are many techniques of image enhancement. Their parameters are traditionally tuned by maximization of SNR criterion, which is unfortunately based on the knowledge of an ideal image. Our approach is based on Hartley entropy, its estimation, and differentiation. Resulting gradient of entropy is estimated without knowledge of ideal images, and it is a subject of minimization. Both SNR maximization and gradient magnitude minimization cause various settings of the given filter. The optimum settings are compared, and their differences are discussed.
Highlights
IntroductionImage quality measurement is important for various image processing tasks
In many different fields, image quality measurement is important for various image processing tasks
The relative changes RC between the qualities of the optimal reconstruction according to the filters Φ1, Φ2, and Φ3 evaluated for the criterion signal-to-noise ratio (SNR) and G are summarized in the Table 7
Summary
Image quality measurement is important for various image processing tasks. Filter performance can be compared by different image quality assessment techniques [3, 4]. Image quality measure signal-to-noise ratio (SNR) [5] or its modifications are the most commonly used to compare filter performance [6,7,8]. SNR measure is based on the knowledge of referential image which is a kind of Full-Reference Image Quality Assessment. No-Reference Image Quality Assessment (NR-IQA) technique [9, 10] must be used to measure image quality. Our approach is focused on relationship between SNR and Hartley entropy. A novel NR-IQA method based on image entropy is introduced and verified on image dataset. Alternative approach focused on motion estimation and parallel computing is included in [11, 12]
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