Kavli Affiliate: Feng Wang
| First 5 Authors: Tianhao Guo, Tianhao Guo, , ,
| Summary:
Single image super-resolution traditionally assumes spatially-invariant
degradation models, yet real-world imaging systems exhibit complex
distance-dependent effects including atmospheric scattering, depth-of-field
variations, and perspective distortions. This fundamental limitation
necessitates spatially-adaptive reconstruction strategies that explicitly
incorporate geometric scene understanding for optimal performance. We propose a
rigorous variational framework that characterizes super-resolution as a
spatially-varying inverse problem, formulating the degradation operator as a
pseudodifferential operator with distance-dependent spectral characteristics
that enable theoretical analysis of reconstruction limits across depth ranges.
Our neural architecture implements discrete gradient flow dynamics through
cascaded residual blocks with depth-conditional convolution kernels, ensuring
convergence to stationary points of the theoretical energy functional while
incorporating learned distance-adaptive regularization terms that dynamically
adjust smoothness constraints based on local geometric structure. Spectral
constraints derived from atmospheric scattering theory prevent bandwidth
violations and noise amplification in far-field regions, while adaptive kernel
generation networks learn continuous mappings from depth to reconstruction
filters. Comprehensive evaluation across five benchmark datasets demonstrates
state-of-the-art performance, achieving 36.89/0.9516 and 30.54/0.8721 PSNR/SSIM
at 2 and 4 scales on KITTI outdoor scenes, outperforming existing methods by
0.44dB and 0.36dB respectively. This work establishes the first
theoretically-grounded distance-adaptive super-resolution framework and
demonstrates significant improvements on depth-variant scenarios while
maintaining competitive performance across traditional benchmarks.
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