Infrared Image Dynamic Range Compression Based on Adaptive Contrast Adjustment and Structure Preservation

The infrared (IR) image dynamic range compression (DRC) technology involves compressing high dynamic range (HDR) IR images into low dynamic range (LDR) images for display on common devices. To facilitate human observation, DRC methods should preserve the structural information as much as possible while adjusting the contrast of HDR IR images. However, existing DRC methods struggle to adapt to various highly dynamic IR scenes when using fixed parameter settings. To address this limitation, a novel gradient domain-based DRC method with adaptive contrast adjustment and structure preservation (ACASP) is proposed. Our ACASP adapts local contrast and gradients by analyzing local features of HDR IR images, effectively handling different HDR IR scenes. We introduce local contrast and variance to enhance visibility in low-contrast areas and preserve details in high-contrast areas. Specifically, we design a contrast-adaptive mapping curve and a gradient-adaptive modulation factor (GMF) to optimize both contrast and structure in the LDR image. Extensive experiments on three public HDR IR datasets demonstrate that the proposed method can outperform state-of-the-art DRC methods in both quantitative and qualitative analyses. This work contributes to the field by offering a more adaptive and robust approach to IR image DRC.