Close-up hyperspectral image focus stacking through Laplacian pyramid fusion

Hyperspectral imaging has emerged as a tool for detailed spectral and image analysis, with applications in various fields. Initially developed for remote sensing applications, this technology utilizes hundreds of spectral channels and is increasingly being used also in laboratory settings. However, laboratory settings present significant challenges to conducting a detailed, close-range analysis of small samples due to a large image magnification of a hyperspectral system and its specified optics. A limited working distance of close-up hyperspectral imaging results in a shallow depth of field, causing blurred regions when imaging non–flat samples. This paper presents a computationally efficient multi-focal-plane fusion algorithm for hyperspectral images. The algorithm integrates complementary spatial information from different focal depths while preserving the reflectance of the original data. The core of the presented hyperspectral focus stacking method is based on Laplacian pyramid decomposition combined with local sharpness metrics using standard deviation statistics. The proposed approach is tuned by three control fusion parameters. These parameters are adjusted and optimized using selected non-reference image quality metrics, such as the Naturalness Image Quality Evaluator (NIQE), the Perceptual Image Quality Evaluator (PIQE), and the Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE), as well as general global sharpness measures and the proposed local sharpness evaluation procedure. Experimental evaluations demonstrate that, with appropriate parameter settings, the fused hyperspectral image consistently exhibits higher gradient-based sharpness than any individual input image, while maintaining spectral integrity. This approach is ideal for close-up, hyperspectral-based laboratory analysis of a variety of samples with limited depth of field.