Wideband DOA estimation has been applied in various signal source location scenarios, e.g., in wireless communication systems to improve the capacity of communication. Existing wideband DOA methods often require prior knowledge such as the number of sources as well as pre-estimations. Moreover, they may suffer from model-mismatch problem. In this paper, we employ the manifold separation technique and Jacobi-Anger expansion to allow multi-frequency joint processing of wideband DOA, which alleviates the challenge of model-mismatch and leads to a much higher DOA resolution. The proposed method is further formulated to be a multi-convex rank-minimization problem to facilitate the analysis of the problem and to improve the convergence performance. The superior performance of the proposed multi-frequency joint processing method has been demonstrated by several numerical studies.

Pan-sharpening aims to spatially enhance the low-resolution multispectral image (LRMS) by transferring high-frequency details from a panchromatic image (PAN) while preserving the spectral characteristics of LRMS. Previous arts mainly focus on how to learn a high-resolution multispectral image (HRMS) on the i.i.d. assumption. However, the distribution of training and testing data often encounters significant shifts in different satellites. To this end, this paper proposes a generalizable pan-sharpening network via domain-irrelevant feature learning. On the one hand, a structural preservation module (STP) is designed to fuse high-frequency information of PAN and LRMS. Our STP is performed on the gradient domain because it consists of structure and texture details that can generalize well on different satellites. On the other hand, to avoid spectral distortion while promoting the generalization ability, a spectral preservation module (SPP) is developed. The key design of SPP is to learn a phase fusion network of PAN and LRMS. The amplitude of LRMS, which contains 'satellite style' information is directly injected in different fusion stages. Extensive experiments have demonstrated the effectiveness of our method against state-of-the-art methods in both single-satellite and cross-satellite scenarios. Code is available at: https://github.com/LYL1015/DIRFL.

Pan-sharpening aims to utilize the high-resolution panchromatic (PAN) image as a guidance to super-resolve the spatial resolution of the low-resolution multispectral (MS) image. The key challenge in pan-sharpening is how to effectively and precisely inject high-frequency edges and textures from the PAN image into the low-resolution MS image. To address this issue, we propose a High-frequency Feature Enhancement and Alignment Network (HFEAN) for effectively encouraging the high-frequency learning. To implement it, three core designs are customized: a Fourier convolution based efficient feature enhancement module (FEM), an implicit neural alignment module (INA), and a preliminary alignment module (Pre-align). To be specific, FEM employs the fast Fourier convolution with attention mechanism to achieve the mixed global-local receptive field on each scale of the high-frequency domain, thus yielding the informative latent codes. INA leverages implicit neural function to precisely align the latent codes from different scales in the continuous domain. In this way, the high frequency signals at different scales are represented as functions of continuous coordinates, enabling a precise feature alignment in a resolution-free manner. Pre-align is developed to further address the inherent misalignment between PAN and MS pairs. Extensive experiments over multiple satellite datasets validate the effectiveness of the proposed network and demonstrate its favorable performance against the existing state-of-the-art methods both visually and quantitatively. Code is available at: https://github.com/Gracewangyy/HFEAN.