A Hyperspectral Image Restoration Method and System Based on Dual-Enhanced Low-Rank Tensor Constraints

CN118710551BActive Publication Date: 2026-05-26CHONGQING UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
CHONGQING UNIV
Filing Date
2024-06-25
Publication Date
2026-05-26

AI Technical Summary

Technical Problem

Existing low-rank tensor constraint methods fail to effectively combine global and non-local priors in the restoration of noisy and missing hyperspectral remote sensing images, resulting in insufficient generalization ability of the restoration model, loss of complementary information between spatial and spectral modes, and easy generation of residual noise.

Method used

A dual-enhanced low-rank tensor constraint method is adopted. Through a global-guided nonlocal learning framework, combined with global and nonlocal priors, and utilizing enhanced global and nonlocal low-rank tensor approximations, a stable tensor quantum space and paired modal complementary fusion are constructed to suppress residual noise and improve image restoration accuracy.

Benefits of technology

It improves the restoration accuracy and generalization ability of hyperspectral remote sensing images, stably characterizes and propagates the inherent structure of images, and reduces the loss of deep information and residual noise, which is superior to existing methods.

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Abstract

This invention discloses a hyperspectral image restoration method and system based on dual-enhanced low-rank tensor constraints, belonging to the field of image processing technology. The steps are as follows: S1: Acquire hyperspectral image data of the target area; S2: Image preprocessing to generate a test image; S3: Input the test image into a dual-enhanced low-rank tensor constraint model to obtain the restored image; wherein the dual-enhanced low-rank tensor constraint model includes a global prior learning module, a nonlocal prior learning module, and a prior integration module. The test image is first input into the global prior learning module to generate a global learning result, then the test image and the global learning result are input into the nonlocal prior learning module to generate a nonlocal learning result, and finally, the prior integration module performs prior weighted integration of the global and nonlocal learning results to obtain the restored image. The effect is that its performance is superior to other hyperspectral remote sensing image restoration methods, and it has a greater advantage in accurately estimating Earth monitoring data.
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