Wavelength- and Depth-Aware Deep Image Prior for Blind Hyperspectral Imagery Deblurring with Coarse Depth Guidance

Hyperspectral imagery (HSI) provides detailed spectral information, enabling precise analysis of materials. However, HSI imaging suffers from blurring degradation which results in the loss of fine details and hinders subsequent applications. The degree of blurriness is highly related to wavelength and depth, existing deblurring methods either lack the utilization of spectral correlation or ignore the depth variation since paired HSI and depth data are difficult to acquire and less discussed, leading to degraded performance when encountering wide-range HSIs of non-planar scenes. To address these challenges in both data acquisition and algorithm design, we propose a novel approach that simultaneously collects both modalities and integrates depth refinement into a blind HSI deblurring model with wavelength- and depth-aware deep image prior. Specifically, we capture blurred HSI and coarse depth map with separate devices, followed by registration. Our method performs depth-guided deblurring through depth-variant multi-channel kernel estimation and soft-weight map-based layer composition, while simultaneously refining the depth. The proposed approach effectively restores fine details with fewer artifacts, showing superior performance for both simulated blurred HSIs and real captured HSIs.