Reasoning segmentation (RS) interprets implicit textual instructions to accurately segment target regions. This reasoning capability transforms ambiguous non-expert queries into precise pixel-level masks, thereby enabling downstream tasks like area measurement and density analysis with a level of precision unattainable by detection methods. However, existing RS models are not tailored for agriculture and lack domain-specific knowledge, which poses challenges in handling similar pest appearances and small target scales. To bridge this gap, we introduce a fine-grained pest RS task with two subtasks: Pest Discriminative Referring Expression Segmentation (PDRES) and Pest Exclusion Reasoning Segmentation (PERS). Based on this, we propose PestScope, which integrates vision, language, and reasoning for fine-grained pest segmentation. To tackle the exclusion of small non-target pests, we introduce a dedicated [NON] token alongside the standard [SEG] token for target pests. This guides the model to prioritize small target pests and suppress non-target background regions. To further address pest similarity, we propose an Exclusivity Suppression Loss, applying differentiated supervision to [SEG] and [NON] tokens to better separate target and non-target pests. Additionally, we develop an automated dataset construction pipeline to address the scarcity of fine-grained, difficulty-controllable pest RS datasets. It produces 45k and 27.6k image-text-mask samples for the PDRES and PERS tasks, respectively, covering 18 pest categories. Experiments show that in small and similar pest scenarios, integrating PestScope into mainstream models improves average gIoU by 4.28% on PDRES and 6.49% on PERS. For unseen pest categories, gIoU increases by 21.72% and 8.66%, respectively, demonstrating strong generalization. Code and datasets will be available at: https://github.com/aluodaydayup/PestScope.

Abstract Traditional dehazing algorithms have long been limited in their ability to remove fog effectively, especially in preserving details. This study proposes an improved dark channel prior dehazing algorithm designed to overcome the limitations of traditional algorithms by refining the transmission map and estimating atmospheric light intensity. The algorithm fully exploits the anti‐haze characteristics of the Gabor filter to extract multi‐directional texture features. By fusing these features to form a guidance map for guided filtering, it effectively reduces the blurring caused by guided filtering on the image edges, thereby producing a more accurate transmission map. Simultaneously, an enhanced atmospheric light successfully reduces interference from white objects in the image. The experiment phase utilized the publicly available RESIDE dataset for validation. The algorithm achieved a Peak Signal‐to‐Noise Ratio (PSNR) of 22.4 and a Structural Similarity Index (SSIM) of 0.88. These metrics indicate the algorithm's superior dehazing capabilities.

Abstract In recent years, research on infrared and visible image fusion has mainly focused on deep learning‐based approaches, particularly deep neural networks with auto‐encoder architectures. However, these approaches suffer from problems such as insufficient feature extraction capability and inefficient fusion strategies. Therefore, this paper introduces a novel image fusion network to address the limitations of infrared and visible image fusion networks with auto‐encoder architectures. In the designed network, the encoder employs a multi‐branch cascade structure, and these convolution branches with different kernel sizes provide the encoder with an adaptive receptive field to extract multi‐scale features. In addition, the fusion layer incorporates a non‐local attention module that is inspired by the self‐attention mechanism. With its global receptive field, this module is used to build a non‐local attention fusion network, which works together with the ‐norm spatial fusion strategy to extract, split, filter, and fuse global and local features. Comparative experiments on the TNO and MSRS datasets demonstrate that the proposed method outperforms other state‐of‐the‐art fusion approaches.

In this paper, we consider the problem of computing bivariate barycentric and Newton rational interpolation over rectangular grids. Given a set of rectangular grids, under the conditions that the degrees of the numerator and denominator of rational interpolant are prescribed, we provide a matrix method for calculating barycentric weight vector and denominator value vector, which, respectively, induce barycentric and Newton representations of rational interpolants. We aim at constructing the rational interpolants that globally approximate interpolated function. Numerical examples compare the maximum errors of barycentric rational interpolant, Newton rational interpolant, linear interpolant, cubic interpolant and spline interpolant. Numerical results show the higher accuracy of barycentric and Newton rational interpolation.

Color-based image processing has been tried as a mean near to human vision, so they have been utilized to realize a likeness to human eyes, for instance in flexible extraction of prominent features. In this paper, a novel approach to image registration called Absent color indexing (ABC) is proposed to address the problem of robust pattern search. Color histogram-based methods have shown favorable performance in the conditions of object rotation, deformation, and occlusion. Most of them have had good performances, but their weakness to fail in distinguishing similar but different objects in the scene. The proposed approach works well in these situations by using absent colors that have relatively low-frequencies or nonexisting colors in color histogram bins. In order to obtain a more accurate positioning, we extended our approach to combine with a correlation filter. Experimental results on Mondrian random patterns and the real-world images show that the proposed ABC has rather high performance for distinguishing similar objects.