MRI contrast enhancement using schrodinger spectrum and unsupervised CNN: Amélioration du contraste IRM par le spectre de Schrödinger et un CNN non supervisé

Ultra-high-field (7T) MRI offers higher intrinsic SNR and resolution than lower-field MRI, yet images still suffer from spatially varying bias fields, intensity non-uniformity, and amplified high-frequency noise, causing local blur, poorer tissue separability, and potential diagnostic errors11. Prior work uses local contrast enhancement (e.g., CLAHE, histogram remapping) but these methods are sensitive to parameters and can over-enhance noise or create haloing/staircase artifacts; γ-based and SWT-BPJHE schemes can likewise co-amplify noise and ring edges at UHF2,3,4,52,3,4,5. We propose a post-acquisition enhancement based on 2D Semi-Classical Signal Analysis (SCSA), which represents images via eigenfunctions of a semi-classical Schrödinger operator and naturally suppresses noise6,76,7. To avoid manual, histogram-based selection, we learn a spatially varying, pixel-wise map G(x,y)G(x,y) with an unsupervised CNN88 and inject it into the SCSA remapping, yielding anatomically aware, adaptive contrast enhancement with reduced noise amplification.