Hyperspectral image restoration based on color superpixel segmentation

Abstract

Hyperspectral images (HSI) are often degraded by various types of noise during the acquisition process, such as Gaussian noise, impulse noise, dead lines and stripes, etc. Recently, there exists a growing attenrion on low-rank matrix/tensor-based methods for HSI data restoration, assuming that the overall data is low-rank. However, the assumption of overall low-rankness often proves inaccurate due to the spatially heterogeneous local similarity characteristics of HSI. Traditional cube-based methods involve dividing the HSI into fixed-size cubes. However, using fixed-size cubes does not provide flexible coverage of locally similar regions at varying scales. Inspired by superpixel segmentation, this paper proposes the Shrink Low-rank Super-tensor (SLRST) approach for HSI recovery. Instead of using fixed-size cubes, SLRST employs a size-adaptive super-tensor. The proposed approach is effectively solved using the Alternating Direction Method of Multipliers (ADMM). Numerical experiments on HSI data verify that the proposed method outperforms other competing methods.

Assessing public street lighting in Morocco: evaluating efficiency and the impact of geometrical parameters on luminance

“…In this phase of designing a road lighting system, the objective is to identify the optimal solution that balances lighting quality and energy efficiency. Also, there is a high potential to develop the street lighting based on renewable energy such as Photovoltaic energy, in order to have a sustainable street lightning [20] and the future work should make an optimization of street lighting based on artificial intelligent decision [22,23].…”