Fusing infrared polarization images for road detection via denoising diffusion probabilistic models.

Recent advancements in road detection using infrared polarization imaging have shown promising results. However, existing methods focus on refined network structures without effectively exploiting infrared polarization imaging mechanisms for enhanced detection. The scarcity of datasets also limits the performance of these methods. In this Letter, we present a denoising diffusion model aimed at improving the performance of road detection in infrared polarization images. This model achieves effective integration of infrared intensity and polarization information through forward and reverse diffusion processes. Furthermore, we propose what we believe to be a novel method to augment polarized images from different orientations based on the angle of polarization. The augmented polarized image serves as the guiding condition, enhancing the robustness of the diffusion model. Our experimental results validate the effectiveness of the proposed method, demonstrating competitive performance compared to state-of-the-art methods, even with fewer training samples.

Noise-aware infrared polarization image fusion based on salient prior with attention-guided filtering network.

Infrared polarization image fusion integrates intensity and polarization information, producing a fused image that enhances visibility and captures crucial details. However, in complex environments, polarization imaging is susceptible to noise interference. Existing fusion methods typically use the infrared intensity (S0) and degree of linear polarization (DoLP) images for fusion but fail to consider the noise interference, leading to reduced performance. To cope with this problem, we propose a fusion method based on polarization salient prior, which extends DoLP by angle of polarization (AoP) and introduces polarization distance (PD) to obtain salient target features. Moreover, according to the distribution difference between S0 and DoLP features, we construct a fusion network based on attention-guided filtering, utilizing cross-attention to generate filter kernels for fusion. The quantitative and qualitative experimental results validate the effectiveness of our approach. Compared with other fusion methods, our method can effectively suppress noise interference and preserve salient target features.

Characterization of inulolytic enzymes from the Jerusalem artichoke-derived Glutamicibacter mishrai NJAU-1.

The rhizosphere context of inulin-accumulating plants, such as Jerusalem artichoke (Helianthus tuberosus), is an ideal starting basis for the discovery of inulolytic enzymes with potential for bio fructose production. We isolated a Glutamicibacter mishrai NJAU-1 strain from this context, showing exo-inulinase activity, releasing fructose from fructans. The growth conditions (pH 9.0; 15 °C) were adjusted, and the production of inulinase by Glutamicibacter mishrai NJAU-1 increased by 90% (0.32 U/mL). Intriguingly, both levan and inulin, but not fructose and sucrose, induced the production of exo-inulinase activity. Two exo-inulinase genes (inu1 and inu2) were cloned and heterologously expressed in Pichia pastoris. While INU2 preferentially hydrolyzed longer inulins, the smallest fructan 1-kestose appeared as the preferred substrate for INU1, also efficiently degrading nystose and sucrose. Active site docking studies with GFn- and Fn-type small inulins (G is glucose, F is fructose, and n is the number of ß (2-1) bound fructose moieties) revealed subtle substrate differences between INU1 and INU2. A possible explanation about substrate specificity and INU's protein structure is then suggested. KEY POINTS: • A Glutamicibacter mishrai strain harbored exo-inulinase activity. • Fructans induced the inulolytic activity in G. mishrai while the inulolytic activity was optimized at pH 9.0 and 15 °C. • Two exo-inulinases with differential substrate specificity were characterized.