The fusion of infrared and visible images together can fully leverage the respective advantages of each, providing a more comprehensive and richer set of information. This is applicable in various fields such as military surveillance, night navigation, environmental monitoring, etc. In this paper, a novel infrared and visible image fusion method based on sparse representation and guided filtering in Laplacian pyramid (LP) domain is introduced. The source images are decomposed into low- and high-frequency bands by the LP, respectively. Sparse representation has achieved significant effectiveness in image fusion, and it is used to process the low-frequency band; the guided filtering has excellent edge-preserving effects and can effectively maintain the spatial continuity of the high-frequency band. Therefore, guided filtering combined with the weighted sum of eight-neighborhood-based modified Laplacian (WSEML) is used to process high-frequency bands. Finally, the inverse LP transform is used to reconstruct the fused image. We conducted simulation experiments on the publicly available TNO dataset to validate the superiority of our proposed algorithm in fusing infrared and visible images. Our algorithm preserves both the thermal radiation characteristics of the infrared image and the detailed features of the visible image.

To solve problems of brightness and detail information loss in infrared and visible image fusion, an effective infrared and visible image fusion method using rolling guidance filtering and gradient saliency map is proposed in this paper. The rolling guidance filtering is used to decompose the input images into approximate layers and residual layers; the energy attribute fusion model is used to fuse the approximate layers; the gradient saliency map is introduced and the corresponding weight matrices are constructed to perform on residual layers. The fusion image is generated by reconstructing the fused approximate layer sub-image and residual layer sub-images. Experimental results demonstrate the superiority of the proposed infrared and visible image fusion method.

Lightweight remote sensing image super-resolution (RSISR) methods aim to reconstruct remote sensing images (RSIs) while reducing computational complexity. Previous lightweight model development has primarily focused on the design of convolutional neural networks (CNNs). While CNNs excel at capturing local features, they are limited in establishing long-range dependencies. Mamba, as a model for long-range modeling, has linear computational complexity, making it a viable option for lightweight models. Based on these considerations, this paper proposes an efficient mamba-attention network (EMAN) that can efficiently capture the intricate details and broader semantic information in RSIs. Specifically, we designed a multi-scale detail extraction unit (MDEU) and a multi-dimensional mamba-attention (MDMA). In MDEU, we introduced a multi-scale mechanism and local variance to focus on structural information in RSIs. In MDMA, we integrated spatial expansion and an atrous-based selective scan mechanism to design an efficient scanning method. This method ensures the lightweight nature of the model while establishing global correlations. Additionally, MDMA establishes inter-channel correlations to enhance information exchange. We conducted a comprehensive evaluation of the proposed method on two remote sensing datasets and five benchmark super-resolution (SR) datasets. Extensive experiments demonstrate that our method can achieve superior performance while maintaining a model complexity similar to other lightweight models.

This paper presents a multi-scale feature-enhanced water detector using truncated Gaussian clutter (TGCFeWD) for SAR imagery. It aims to improve detection accuracy through adaptive elimination of high-intensity outliers and shadows. High-intensity outliers cause over-estimation of parameters used for water data statistical modeling, resulting in inaccurate thresholds. Furthermore, shadows in SAR imagery have similar digital numbers and statistical parameters to water, making them difficult to distinguish. To address these two key issues, this paper proposes a comprehensive approach comprised of three main components: The Part A is to calculate Gaussian distance based on which the land surfaces or artificial objects can be removed easily by Otsu. The Part B is to expand the difference between water and shadows through feature enhancing. The Part C is to segment water and shadows according to the expanding differences. The proposed TGCFeWD method effectively detects water bodies including seas, rivers and lakes. Compared to several existing methods, TGCFeWD greatly improves water detection accuracy in complex environments. Based on metrics of Ac, F1 and mIoU, TGCFeWD achieves the best performance (92.4%, 82.4%, 80.1% for all data with five water body types) compared to several traditional methods, and even outperforms some neural network-based methods in certain scenarios. The results are validated on the HISEA Flooding dataset.

In complex environments, the clutter statistical characteristics of synthetic aperture radar (SAR) are inconstant, and the constant detection performance of a false alarm rate (CFAR) detector based on a clutter statistical model is also hard to achieve. As a result, the overestimated threshold leads to a degradation in detection probability. To this end, this paper proposes a SAR ship detector different from CFAR detectors, which is independent of traditional clutter statistical distribution models and the probability of a false alarm (PFA). The proposed detector aims to raise the ship detection probability and alleviate interference from complex environments such as multi-target areas, shores, and breakwaters. It estimates clutter-truncated thresholds based on clutter intensity statistics (CIS). Firstly, three statistical parameters, including the mean, standard deviation, and maximum intensity of background clutter contaminated by outliers, are calculated; secondly, these parameters are utilized to estimate the clutter-truncated threshold using the novel CIS; and finally, the pixel under test is determined according to the CIS detection rule. Compared with CFAR-based algorithms, CIS obtains a high probability of detection in complex environments. As for other aspects, the CIS detector is insensitive to the structure of the detection window, as well as the size. It is also computationally efficient due to its simple calculations. The superiority of the CIS detector is validated on scene-differed SAR images from the DSSDD dataset.

In the field of single remote sensing image Super-Resolution (SR), deep Convolutional Neural Networks (CNNs) have achieved top performance. To further enhance convolutional module performance in processing remote sensing images, we construct an efficient residual feature calibration block to generate expressive features. After harvesting residual features, we first divide them into two parts along the channel dimension. One part flows to the Self-Calibrated Convolution (SCC) to be further refined, and the other part is rescaled by the proposed Two-Path Channel Attention (TPCA) mechanism. SCC corrects local features according to their expressions under the deep receptive field, so that the features can be refined without increasing the number of calculations. The proposed TPCA uses the means and variances of feature maps to obtain accurate channel attention vectors. Moreover, a region-level nonlocal operation is introduced to capture long-distance spatial contextual information by exploring pixel dependencies at the region level. Extensive experiments demonstrate that the proposed residual feature calibration network is superior to other SR methods in terms of quantitative metrics and visual quality.