A new hybrid image denoising algorithm using adaptive and modified decision-based filters for enhanced image quality.

Denoising is one of the most important processes in digital image processing to recover visual quality and structural integrity in images. Traditional methods often suffer from limitations like computational complexity, over-smoothing, and the inability to preserve critical details, particularly edges. This paper introduces a hybrid denoising algorithm combining Adaptive Median Filter (AMF) and Modified Decision-Based Median Filter (MDBMF) to address these challenges. The AMF adjusts the window sizes dynamically to precisely detect noisy pixels, and MDBMF selectively recovers corrupted pixels without affecting intact regions, effectively reducing noise while preserving edges. The subjective analysis is supplemented with objective analyses in which visual quality proves that hybrid approach performance considerably outperforms existing state-of-the-art methods. The test is conducted on nine benchmark images standard and medical dataset, namely, Chest and Liver images with different noise densities in the range from 10 to 90%. Quantitative evaluations PSNR, MSE, IEF, SSIM, FOM and VIF clearly show the performance superiority of the hybrid approach when compared to the state-of-the-art approaches. The improvement in PSNR was up to 2.34 dB, IEF improvement was more than 20%, and the improvement in MSE was up to 15% improvement over other methods like BPDF, AT2FF, and SVMMF. Improvement in the values of SSIM is up to 0.07, which confirms improved structural similarity. Furthermore, the FOM and VIF metrics demonstrate the remarkable performance of the hybrid approach: both the FOM and VIF exceeded all other denoising techniques evaluated, reaching 0.68 and 0.61, respectively.