All-in-focus(AIF) images, which contain comprehensive scene information with global sharpness, play a crucial role in high-precision light field (LF) measurement and computational imaging. However, generating AIF images from LF data typically requires accurate depth priors, which are often unavailable or unreliable in practice. To overcome this limitation, directly fusing a series of LF refocused images provides an effective alternative that eliminates the dependency on explicit depth estimation. Nevertheless, existing multi-focus image fusion(MFIF) methods are primarily designed for fusing image pairs with complementary focus, performing poorly when applied to stacks due to the error accumulation that occurs during iterative fusion. To this end, we propose a Frequency-Decoupled Stack Fusion Network (FDSNet) for high-precision depth-free LF AIF image generation. FDSNet incorporates a spatial-frequency joint feature extraction module that captures multi-scale spatial details while decoupling high- and low- frequency components to model textures and contextual information separately, thereby alleviating edge blurring caused by subtle focal variations and weak textures in transition regions. Moreover, a dual-stage cross-attention fusion module, following a coarse-to-fine strategy, suppresses artifacts, enhances edge fidelity, and enables simultaneous fusion of arbitrary numbers of refocused images, thereby avoiding error accumulation and computational redundancy. Extensive experiments on both synthetic and real LF datasets demonstrate that FDSNet achieves superior visual quality and quantitative performance. Additional experiments further demonstrate that FDSNet performs robustly under varying low-light and noisy conditions. These results validate that FDSNet delivers excellent fusion capability in terms of image clarity, detail preservation, noise resistance, and generalization, outperforming existing state-of-the-art methods.

Accurate multi-source remote sensing image matching is the foundation of many applications. However, the complex geometric and nonlinear intensity distortion between remote sensing images makes it challenging. This work selects 11 representative ones: OS-SIFT, PSO-SIFT, LGHD, LINIFT, RIFT, NISR, POS-GIFT, RedFeat, MatchFormer, and SemLA, and compares their performance on various remote sensing images. At last, we summarize the work by giving the research bottleneck and potential research directions toward high-precision multisource remote image matching.

The accurate matching of multisource, multi-temporal remote sensing images is challenging because of significant nonlinear intensity differences (NIDs) and severe geometric distortions. To address these problems, we developed a robust image matching method: oriented filter-based matching (OFM). OFM is insensitive to NIDs, while exhibiting scale and rotational invariance. First, salient feature points with multiscale attributes were detected in the Gaussian-scale space of the input images. Then, the images were convoluted using multi-oriented filters, and unified feature maps were constructed by the extraction of orientation indices using effective data pooling operations. The constructed feature maps were highly resistant to NIDs. Five filters were integrated into the OFM framework to investigate their applicabilities in different application scenarios. Next, a novel rotation-invariant feature descriptor was constructed, using a dominant direction determination approach and a descriptor-grouping strategy. The dominant direction determination approach enables accurate dominant direction estimation, whereas the descriptor-grouping strategy improves the stability of the method under different rotational angles. Finally, brute-force matching was implemented to obtain initial matches; an improved mismatch elimination method was used to identify reliable putative matches. To evaluate the performance of OFM, we created a large dataset comprising 4,427 pairs of multitemporal optical–optical, optical–synthetic aperture radar (SAR), optical–infrared, and optical–depth images. OFM outperformed state-of-the-art methods in terms of number of correct matches, recall, inlier ratio, root mean square error and success rate. Our implement is publicly available1.

Multimodal remote sensing image (MRSI) high-precision matching faces significant challenges due to the large nonlinear distortions in radiation and geometry. To address this problem, we propose a novel MRSI matching method, multimodal remote sensing image matching with Delaunay triangulation constraint network (M2DT-Net), which takes advantage of deep convolutional neural networks (CNNs) and the Delaunay triangulation (DT) strategy. First, a fine-tuned CNN model is applied to learn invariant features with high robustness across MRSIs. Second, an adaptive feature descriptor distance constraint combined with the DEGENSAC refinement is employed to filter the obtained matches, and only the matches with high credibility are retained. Finally, a DT strategy is adopted to expand more underlying correct matches that are dropped inliers in the previous stage. The experimental results on seven types of MRSI pairs indicate that M2DT-Net is superior to seven state-of-the-art image matching algorithms in terms of model performance and efficiency, achieving a balance of robustness and time consumption. The image registration results further demonstrate the effectiveness of the proposed algorithm. Therefore, the proposed M2DT-Net method provides a reference for resolving common multimodal image matching problems.