Enhancing infrared imaging systems with temperature-dependent nonuniformity correction via single-frame and...

Area of Science:

• Image Processing
• Computer Vision
• Infrared Imaging

Background:

• Temperature-dependent nonuniformity degrades infrared image quality.
• Existing variational models for nonuniformity correction (NUC) have limitations due to insufficient use of image intensity characteristics.
• Current methods often rely solely on single-frame gradient priors.

Purpose of the Study:

• To introduce a novel variational model for infrared image nonuniformity correction (NUC).
• To leverage both single-frame and inter-frame structural similarity (SISB) for improved correction.
• To address the limitations of existing NUC methods.

Main Methods:

• A new variational model for NUC is proposed.
• The model utilizes single-frame and inter-frame structural similarity (SISB).
• It exploits structural similarities between the corrected image, bias map, and degraded image.

Main Results:

• The proposed SISB-based variational model effectively suppresses intensity nonuniformity.
• The method demonstrates superior performance compared to state-of-the-art correction models.
• It shows efficient correction capabilities in real-world scenarios.

Conclusions:

• The novel variational model using SISB offers an effective solution for infrared image nonuniformity.
• This approach overcomes limitations of existing methods by utilizing inter-frame information.
• The proposed method represents a significant advancement in infrared image NUC.