Images captured in harsh environments often exhibit blurred details, reduced contrast, and color distortion, which hinder feature detection and matching, thereby affecting the accuracy and robustness of homography estimation. While visual enhancement can improve contrast and clarity, it may introduce visual-tolerant artifacts that obscure the structural integrity of images. Considering the resilience of semantic information against environmental interference, we propose a semantic-driven feature enhancement network for robust homography estimation, dubbed SeFENet. Concretely, in our homography estimation network —— Target Aware Homography Estimation Module(TAHEM), we first introduce an innovative hierarchical scale-aware module to expand the receptive field by aggregating multi-scale information, thereby effectively extracting image’s structural features under diverse harsh conditions. Subsequently, we employ a Semantic Extraction Module to extract multi-scale semantic features from the input images. Combined with a high-level perceptual framework, this enables degradation-tolerant semantic feature extraction. Building upon this, the Semantic-Guide Meta Constraints module leverages a meta-learning training strategy to effectively fuse the semantic features with structural features. By internal-external alternating optimization, the proposed network achieves implicit semantic-wise feature enhancement, thereby improving the robustness of homography estimation in adverse environments by strengthening the local feature comprehension and context information extraction. Experimental results under both normal and harsh conditions demonstrate that SeFENet significantly outperforms SOTA methods, reducing point match error by at least 41% on the large-scale datasets.

Image stitching synthesizes images captured from multiple perspectives into a single image with a broader field of view. The significant variations in object depth often lead to large parallax, resulting in ghosting and misalignment in the stitched results. To address this, we propose a depth-consistency-constrained seamless-free image stitching method. First, to tackle the multi-view alignment difficulties caused by parallax, a multi-stage mechanism combined with global depth regularization constraints is developed to enhance the alignment accuracy of the same apparent target across different depth ranges. Second, during the multi-view image fusion process, an optimal stitching seam is determined through graph-based low-cost computation, and a soft-seam region is diffused to precisely locate transition areas, thereby effectively mitigating alignment errors induced by parallax and achieving natural and seamless stitching results. Furthermore, considering the computational overhead in the shift regression process, a reparameterization strategy is incorporated to optimize the structural design, significantly improving algorithm efficiency while maintaining optimal performance. Extensive experiments demonstrate the superior performance of the proposed method against the existing methods. Code is available at https://github.com/DLUT-YRH/DSFN.

Deep learning-based image stitching methods have achieved promising performance on conventional stitching datasets. However, real-world scenarios may introduce challenges such as complex weather conditions, illumination variations, and dynamic scene motion, which severely degrade image quality and lead to significant misalignment in stitching results. To solve this problem, we propose an adverse condition-tolerant image stitching network, dubbed ACDIS. We first introduce a bidirectional consistency learning framework, which ensures reliable alignment through an iterative optimization paradigm that integrates differentiable image restoration and Gaussian-distribute encoded homography estimation. Subsequently, we incorporate motion constraints into the seamless composition network to produce robust stitching results without interference from moving scenes. We further propose the first adverse scene image stitching dataset, which covers diverse parallax and scenes under low-light, haze, and underwater environments. Extensive experiments show that the proposed method can generate visually pleasing stitched images under adverse conditions, outperforming state-of-the-art methods.

Infrared and visible image alignment is essential and critical to the fusion and multi-modal perception applications. It addresses discrepancies in position and scale caused by spectral properties and environmental variations, ensuring precise pixel correspondence and spatial consistency. Existing manual calibration requires regular maintenance and exhibits poor portability, challenging the adaptability of multi-modal application in dynamic environments. In this paper, we propose a harmonized representation based infrared and visible image alignment, achieving both high accuracy and scene adaptability. Specifically, with regard to the disparity between multi-modal images, we develop an invertible translation process to establish a harmonized representation domain that effectively encapsulates the feature intensity and distribution of both infrared and visible modalities. Building on this, we design a hierarchical framework to correct deformations inferred from the harmonized domain in a coarse-to-fine manner. Our framework leverages advanced perception capabilities alongside residual estimation to enable accurate regression of sparse offsets, while an alternate correlation search mechanism ensures precise correspondence matching. Furthermore, we propose the first ground truth available misaligned infrared and visible image benchmark for evaluation. Extensive experiments validate the effectiveness of the proposed method against the state-of-the-arts, advancing the subsequent applications further. Code and dataset are available at https://github.com/Jzy2017/HR4IR.

Image stitching is a critical task in panorama perception that involves combining images captured from different viewing positions to reconstruct a wider field-of-view (FOV) image. Existing visible image stitching methods suffer from performance drops under severe conditions since environmental factors can easily impair visible images. In contrast, infrared images possess greater penetrating ability and are less affected by environmental factors. Therefore, we propose an infrared and visible image-based multispectral image stitching method to achieve all-weather, broad FOV scene perception. Specifically, based on two pairs of infrared and visible images, we employ the salient structural information from the infrared images and the textual details from the visible images to infer the correspondences within different modality-specific features. For this purpose, a multiscale progressive mechanism coupled with quadrature correlation is exploited to improve regression in different modalities. Exploiting the complementary properties, accurate and credible homography can be obtained by integrating the deformation parameters of the two modalities to compensate for the missing modality-specific information. A global-aware guided reconstruction module is established to generate an informative and broad scene, wherein the attentive features of different viewpoints are introduced to fuse the source images with a more seamless and comprehensive appearance. We construct a high-quality infrared and visible stitching dataset for evaluation, including real-world and synthetic sets. The qualitative and quantitative results demonstrate that the proposed method outperforms the intuitive cascaded fusion-stitching procedure, achieving more robust and credible panorama generation. Code and dataset are available at https://github.com/Jzy2017/MSGA.

Multi-spectral image stitching leverages the complementarity between infrared and visible images to generate a robust and reliable wide field-of-view~(FOV) scene. The primary challenge of this task is to explore the relations between multi-spectral images for aligning and integrating multi-view scenes. Capitalizing on the strengths of Graph Convolutional Networks (GCNs) in modeling feature relationships, we propose a spatial graph reasoning based multi-spectral image stitching method that effectively distills the deformation and integration of multi-spectral images across different viewpoints. To accomplish this, we embed multi-scale complementary features from the same view position into a set of nodes. The correspondence across different views is learned through powerful dense feature embeddings, where both inter- and intra-correlations are developed to exploit cross-view matching and enhance inner feature disparity. By introducing long-range coherence along spatial and channel dimensions, the complementarity of pixel relations and channel interdependencies aids in the reconstruction of aligned multi-view features, generating informative and reliable wide FOV scenes. Moreover, we release a challenging dataset named ChaMS, comprising both real-world and synthetic sets with significant parallax, providing a new option for comprehensive evaluation. Extensive experiments demonstrate that our method surpasses the state-of-the-arts.

Underwater images suffer from light refraction and absorption, which impairs visibility and interferes the subsequent applications. Existing underwater image enhancement methods mainly focus on image quality improvement, ignoring the effect on practice. To balance the visual quality and application, we propose a heuristic normalizing flow for detection-driven underwater image enhancement, dubbed WaterFlow. Specifically, we first develop an invertible mapping to achieve the translation between the degraded image and its clear counterpart. Considering the differentiability and interpretability, we incorporate the heuristic prior into the data-driven mapping procedure, where the ambient light and medium transmission coefficient benefit credible generation. Furthermore, we introduce a detection perception module to transmit the implicit semantic guidance into the enhancement procedure, where the enhanced images hold more detection-favorable features and are able to promote the detection performance. Extensive experiments prove the superiority of our WaterFlow, against state-of-the-art methods quantitatively and qualitatively.

Image stitching is a fundamental task that requires multiple images from different viewpoints to generate a wide field-of-viewing~(FOV) scene. Previous methods are developed on RGB images. However, the severe weather and harsh conditions, such as rain, fog, low light, strong light, etc., on visible images may introduce evident interference, leading to the distortion and misalignment of the stitched results. To remedy the deficient imaging of optical sensors, we investigate the complementarity across infrared and visible images to improve the perception of scenes in terms of visual information and viewing ranges. Instead of the cascaded fusion-stitching process, where the inaccuracy accumulation caused by image fusion hinders the stitch performance, especially content loss and ghosting effect, we develop a learnable feature adaptive network to investigate a stitch-oriented feature representation and perform the information complementary at the feature-level. By introducing a pyramidal structure along with the global fast correlation regression, the quadrature attention based correspondence is more responsible for feature alignment, and the estimation of sparse offsets can be realized in a coarse-to-fine manner. Furthermore, we propose the first infrared and visible image based multi-spectral image stitching dataset, covering a more comprehensive range of scenarios and diverse viewing baselines. Extensive experiments on real-world data demonstrate that our method reconstructs the wide FOV images with more credible structure and complementary information against state-of-the-arts.