{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T20:53:22Z","timestamp":1782939202636,"version":"3.54.5"},"reference-count":50,"publisher":"World Scientific Pub Co Pte Ltd","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2025,9,30]]},"abstract":"<jats:p> Images are corrupted by the Additive White Gaussian Noise (AWGN) from imaging devices. Existing spatial-domain filters often introduce edge distortion at high noise levels. We propose the Inverse Gaussian Harmonic Filter (IGHF), a novel training-free denoising framework that inverts the Gaussian kernel and employs harmonic weighting on standard deviation terms. The first closed-form mathematical model provides superior edge preservation while effectively suppressing noise. We evaluate hybrid approaches combining IGHF with BM3D [K. Dabov, A. Foi, V. Katkovnik and K. Egiazarian, Image denoising by sparse 3-D transform-domain collaborative filtering, IEEE Trans. Image Process. 8 (2007) 2080\u20132095.] and DnCNN [K. Zhang K, W. Zuo W, Y. Chen, D. Meng and L. Zhang, Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising, IEEE Trans. Image Process. 7 (2017) 3142\u20133155.], achieving up to 1.50[Formula: see text]dB PSNR gain over the state-of-the-art spatial methods. Quantitative evaluation on eight benchmark images demonstrates effectiveness across noise levels ([Formula: see text]\u20130.05). Triple-Hybrid fusion achieves 31.96[Formula: see text]dB PSNR with superior SSIM (0.912) and edge-preservation FOM (0.850), outperforming BM3D by 0.35[Formula: see text]dB through systematic grid-search optimization. Runtime analysis confirms real-time capability: Enhanced IGHF processes HD frames in [Formula: see text]\u00a0[Formula: see text]ms with minimal memory footprint ([Formula: see text]\u00a0[Formula: see text]MB), enabling consumer-grade deployment. The deterministic approach provides a novel mathematical foundation for spatial-domain denoising with applications in deep-learning architectures. <\/jats:p>","DOI":"10.1142\/s0218001425340018","type":"journal-article","created":{"date-parts":[[2025,6,13]],"date-time":"2025-06-13T00:11:24Z","timestamp":1749773484000},"source":"Crossref","is-referenced-by-count":1,"title":["Inverse Gaussian Harmonic Filter (IGHF): Spatial Filter with Contrast Stretching Priority"],"prefix":"10.1142","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9507-3250","authenticated-orcid":false,"given":"Fatih","family":"Marasli","sequence":"first","affiliation":[{"name":"Department of Pazar Vocational School, Gaziosmanpasa University, Pazar, Tokat 60800, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0309-3420","authenticated-orcid":false,"given":"Serkan","family":"Ozturk","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Erciyes University Talas Cad. 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