• 3D reconstruction from 2D projections became a central computational imaging pattern, integrating Fourier/spectral methods, convolutional approaches, and iterative projection techniques across electron microscopy and medical tomography [3], [8], [11], [15], [16].

• Digital image processing established a unifying toolkit for image enhancement, analysis, and restoration, blending processing pipelines, deconvolution, and least-squares formulation across decades [2], [6], [12], [13].

• Pattern recognition and scene analysis emerged as a core theme, driving texture-based classification, edge-aware detection, and relaxation-based labeling to interpret scenes [4], [5], [10], [14], [19].

• Edge/curve detection and texture descriptors served as foundational primitives enabling higher-level recognition and reconstruction tasks in computational imaging [4], [5], [14].

• Inverse-problem and spectral-domain perspectives underpinned many reconstruction/restoration efforts, including deconvolution, least squares, and Fourier-based head-section methods [1], [6], [11], [12].