A NEW PROPOSED MODEL FOR IMAGE ENHANCEMENT USING THE UPPER BOUNDS OF CERTAIN COEFFICIENTS OBTAINED BY A SUBCLASS OF THE CHEBYSHEV POLYNOMIALS

This paper introduces a novel image enhancement framework called the Chebyshev Subclass Coefficient Processing (CSCP) model, which is based on the upper bounds of coefficients derived from analytic functions associated with Chebyshev polynomials. The proposed approach establishes a connection between complex analysis and digital image processing by transforming theoretical coefficient bounds into practical convolution masks. The enhancement process employs directional masks operating at multiple angles (0°, 45°, 90°, 135°) with coefficients ζ1, ζ2, ζ3 derived from the subclass N (λ, β, t). The Whale Optimization Algorithm (WOA) is used to optimize the enhancement parameters. Experimental evaluation using Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Contrast Improvement Ratio (CIR), and Absolute Mean Brightness Error (AMBE) metrics demonstrates that our method achieves superior performance compared to conventional enhancement techniques, particularly at optimal parameter configurations.