In the 6G era, user demand for low-latency, cost-effective extreme services such as extended reality (XR) and holographic communications has significantly increased. Multi-domain networks, known for their vast capacity and coverage, are essential in fulfilling the growing demand for high-performance services. Despite their potential, these networks face challenges with domain isolation, requiring a software defined network (SDN) controller for inter-domain communication. Network function virtualization (NFV) enhances flexibility of service delivery with customizable service function chain (SFC), yet prior research falls short in delivering low-latency, cost-efficient services in multi-domain NFV networks alongside an unreasonable assumption that software on physical nodes can support the execution of all virtualization network functions (VNFs). In this paper, we study the problem of SFC deployment with VNF-dependent software migration (SD-VDSM) in multi-domain networks. Particularly, we first formulate the problem by setting an objective to minimize the end-to-end communication delay and the associated costs of service provisioning, while simultaneously ensuring load balancing across multi-domain networks. However, complexity of the issue escalates to an intractable level due to the intertwined nature of SFC deployment strategies and VNF-dependent software migration tactics, which mutually influence each other intricately. To tackle this issue, we propose an innovative heuristic algorithm, designated as the Joint SFC Deployment with VNF-Dependent Software Migration Algorithm (JSD-VDSMA). Comprising three fundamental steps, this algorithm is crafted to adeptly resolve the complexities of service provisioning across multi-domain networks. A suite of rigorous experimental assessments is detailed, demonstrating the capability of our proposed JSD-VDSMA. Through these comparative analyses, we demonstrate its effectiveness not only to increase the service acceptance rate but also to diminish both the end-to-end communication delay and resource utilization costs in comparison to its counterparts.

Simultaneously recovering hyperspectral images (HSIs) from mixed degradations is a classical inverse problem, which has attracted major research efforts. Low-rank matrix recovery (LRMR) has been proved to be an effective method. This paper proposes a multi-scale recovering model based on 3D Gaussian pyramid decomposition and residual reconstitution to improve the LRMR on its adaptability of local correlative noises (including stripes, dead lines and impulse noise) removal. By compressing the HSI cube to lower-resolution layers in spatial domain, the non-local low rank property of clean HSI can be better utilized. LRMR algorithm is applied from the top. The following layers are reconstituted with the upper recovery result and their original decomposition residual, and then executed LRMR until the bottom of the pyramid. In the proposed procedure, mixed noises are removed progressively in terms of frequency, besides the details of clean HSI are well preserved. Experimental results on simulated and real data in terms of qualitative and quantitative assessments show significant improvements over conventional methods.

In recent years, the specialized destriping methods for remote-sensing image emphasize efficiency and flexibility but lose the particulars in wide stripes. Though many excellent inpainting algorithms have been proposed, most of them are too complex and inefficient for hyperspectral image (HSI). In this paper, we propose a novel automatic completing method which is appropriate for the feature of HSI and easy to be implemented in parallel. The boundary information in broken region is restored primarily based on SVM with the input of the surrounding detected edges. Then we introduce an adaptive morphological filling operator restrained by the boundaries to estimate the pixels in the same position of all bands synchronously. Experimental results show satisfactory performance of the proposed system and its significance on HSI completion.

Hyperspectral images (HSIs) are often contaminated by noise, some spectral bands are highly corrupted that they are usually discarded before processing. To make full use of hyperspectral data, a new bidirectional gradient (BG)-prediction-based HSI junk bands restoration algorithm is proposed. Firstly, according to the field spectral reflectance curves continuity and high spectral resolution instruments, both sides of the junk bands reflectance relative to wavelength gradients can be estimated respectively. Thus, calculate the two estimates of each junk band. Finally, followed by introducing the weighting factor which is inversely proportion to the square of wavelength difference and weighting the two estimates, the results of BG-prediction can be obtained. Experiments are implemented using the HIS collected by airborne visible/infrared imaging spectrometer (AVIRIS). Results indicate that compared with linear prediction, bidirectional gradient prediction can effectively improve the restoration performance, meanwhile the ground classification accuracy of the restored HSIs are improved.