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<p>This study presents a novel framework for Super-Resolution (SR) enhancement of Infrared (IR) images. The proposed method employs Second Kernel Lanczos Interpolation (SKLI) for up-sampling low-resolution (LR) IR images. The SKLI technique enables precise image shifting by fractional sampling intervals and is also suitable for multivariate interpolation tasks, such as rotation and geometric transformation of IR images. The primary goal of the SKLI-based approach is to overcome hardware limitations that typically restrict the acquisition of high-resolution IR images. By integrating SKLI within a large-scale data processing framework, this method effectively reconstructs higher-quality SR images from LR inputs. To evaluate the performance of the proposed approach, two main assessment metrics are utilized: Peak Signal-to-Noise Ratio (PSNR) and computation time. The experimental results confirm that the proposed scheme successfully generates high-resolution IR images with superior visual quality and computational efficiency compared to traditional interpolation methods.</p>

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Infrared images processing based on super resolution

  • 期刊论文

  • 发表日期：

    2026年5月29日

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  • H. I. Ashiba

摘要

This study presents a novel framework for Super-Resolution (SR) enhancement of Infrared (IR) images. The proposed method employs Second Kernel Lanczos Interpolation (SKLI) for up-sampling low-resolution (LR) IR images. The SKLI technique enables precise image shifting by fractional sampling intervals and is also suitable for multivariate interpolation tasks, such as rotation and geometric transformation of IR images. The primary goal of the SKLI-based approach is to overcome hardware limitations that typically restrict the acquisition of high-resolution IR images. By integrating SKLI within a large-scale data processing framework, this method effectively reconstructs higher-quality SR images from LR inputs. To evaluate the performance of the proposed approach, two main assessment metrics are utilized: Peak Signal-to-Noise Ratio (PSNR) and computation time. The experimental results confirm that the proposed scheme successfully generates high-resolution IR images with superior visual quality and computational efficiency compared to traditional interpolation methods.