Exploring Chromatic Aberration and Defocus Blur for Relative Depth Estimation From Monocular Hyperspectral Image

Area of Science:

• Computer Vision
• Image Processing
• Optical Physics

Background:

• Depth estimation from monocular images is challenging.
• Hyperspectral images (HSI) present unique challenges due to low resolution and noise.
• Leveraging spectral and spatial information in HSI for depth estimation remains an open area.

Purpose of the Study:

• To develop a framework for relative depth estimation using spectral chromatic and spatial defocus aberrations in monocular HSI.
• To explore intrinsic and extrinsic reflectance properties within HSI for depth estimation.
• To address the challenges of low resolution and noise in HSI for depth estimation.

Main Methods:

• Integrating chromatic aberration and band-wise defocus blur across HSI bands for sparse depth map estimation.
• Employing manifold learning to combine sparse depth maps into an optimized sparse depth map.
• Utilizing graph Laplacian and material properties for dense depth map generation from sparse depth cues.

Main Results:

• Successfully exploited HSI properties to generate reliable depth cues.
• Developed novel methods for estimating sparse and dense depth maps.
• Demonstrated superior performance compared to state-of-the-art RGB image-based depth estimation approaches.

Conclusions:

• The proposed framework effectively utilizes HSI-specific aberrations for accurate relative depth estimation.
• The integration of spectral and spatial cues offers a robust approach to depth mapping in challenging HSI data.
• This work advances depth estimation techniques by leveraging the rich information content of hyperspectral imaging.