In spectrometer measurement, it is very important to accurately calibrate the wavelength of all target characteristic spectra. Although wavelength calibration methods have long been investigated, no techniques have been designed for the scanning, double-layer, secondary diffraction, linear-array CCD spectrometer, to the best of our knowledge. Based on the grating diffraction equation and experimental results, a mathematical model of wavelength calibration was established for the scanning, double-layer, secondary diffraction, linear-array CCD spectrometer. In this study, a robust, full-screen, wavelength calibration algorithm is proposed, based on the related working principle and the requirements of both accuracy and robustness. The detailed steps are as follows. First, we established a wavelength calibration model at central pixels, following the grating diffraction equation. Then, according to the relationship between the difference in pixels and the feedback values of the grating ruler, a model was established to show the association between these factors. Finally, we combined the two models and built a full-screen wavelength calibration model. We theoretically and experimentally demonstrate that the proposed calibration algorithm is an excellent calibration tool, which can conveniently and accurately calibrate the wavelengths of central and non-central pixels at the same time.

Multi-modal image fusion synthesizes information from multiple sources into a single image, facilitating downstream tasks such as semantic segmentation. Current approaches primarily focus on acquiring informative fusion images at the visual display stratum through intricate mappings. Although some approaches attempt to jointly optimize image fusion and downstream tasks, these efforts often lack direct guidance or interaction, serving only to assist with a predefined fusion loss. To address this, we propose an ``Unfolding Attribution Analysis Fusion network'' (UAAFusion), using attribution analysis to tailor fused images more effectively for semantic segmentation, enhancing the interaction between the fusion and segmentation. Specifically, we utilize attribution analysis techniques to explore the contributions of semantic regions in the source images to task discrimination. At the same time, our fusion algorithm incorporates more beneficial features from the source images, thereby allowing the segmentation to guide the fusion process. Our method constructs a model-driven unfolding network that uses optimization objectives derived from attribution analysis, with an attribution fusion loss calculated from the current state of the segmentation network. We also develop a new pathway function for attribution analysis, specifically tailored to the fusion tasks in our unfolding network. An attribution attention mechanism is integrated at each network stage, allowing the fusion network to prioritize areas and pixels crucial for high-level recognition tasks. Additionally, to mitigate the information loss in traditional unfolding networks, a memory augmentation module is incorporated into our network to improve the information flow across various network layers. Extensive experiments demonstrate our method's superiority in image fusion and applicability to semantic segmentation.