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.

Roads play a vital role in rural economic development. However, rural roads feature irregular curvature changes, are narrow, and are built using diverse construction materials, which renders the analysis of road geometric and spectral characteristics less certain and reduces the automation ability of existing methods. Thus, this letter proposes a semiautomatic extraction of rural roads from high-resolution remote sensing images based on a multifeature combination. First, to address irregular curvature change characteristics of rural roads, we modify multiscale line segment orientation histogram (MLSOH) descriptors to reduce the impact of local curvature change on tracking. Second, we design a multicircle template to analyze the contrast between roads and nonroads and solve the problem of narrow roads. Last, we propose a panchromatic and hue, saturation, value (HSV) space interactive matching model to solve the problem of matching diverse road construction materials. This letter employs Pleiades satellite and GF-2 imagery. Compared with other methods, the proposed method improves automated road extraction by ensuring the extraction accuracy.

High-resolution optical cameras have always been important scientific payloads in Mars exploration missions, and the Mars topographic data produced by their detection data can provide support for scientific research on Mars geomorphology and geological structure evolution. As of December 2021, there are still relatively few high-resolution image data at the sub-meter level on the Martian surface, with about 2.6% global coverage and even more limited stereo coverage (just about 0.4%). At the same time, there are still some difficulties in data acquisition and terrain reconstruction processing methods for high-resolution Mars images that need to be solved. This paper described how we designed the in-orbit stereo imaging strategy based on the characteristics of the High Resolution Imaging Camera (HiRIC) of China’s first Mars exploration mission (Tianwen-1), studied the technical solutions for HiRIC stereo image photogrammetry processing, and produced a topographic dataset for the “Tianwen-1” landing area, including digital orthophoto map (DOM) with a GSD (ground sample distance) of 0.7, 3.5m, and digital elevation model (DEM) with a GSD of 3.5m. Precision analysis results show that this topographic data has good consistency in planar position and elevation compared with the existing Mars terrain data, and has advantages in spatial resolution and terrain detail expression, which will be widely used in the geological background study of the “Tianwen-1” landing area, as well as landing site positioning, Martian surface remote operation planning and other Mars scientific research and engineering tasks.