Xiaohang Zhao

Enhancing low-light remote sensing images is crucial for preserving the accuracy and reliability of downstream analyses in a wide range of applications. Although numerous enhancement algorithms have been developed, many fail to effectively address the challenges posed by non-uniform illumination in low-light scenes. These images often exhibit significant brightness inconsistencies, leading to two primary problems: insufficient enhancement in darker regions and over-enhancement in brighter areas, frequently accompanied by color distortion and visual artifacts. These issues largely stem from the limitations of existing methods, which insufficiently account for non-uniform atmospheric attenuation and local brightness variations in reflectance estimation. To overcome these challenges, we propose a robust enhancement method based on non-uniform illumination compensation and the Atmospheric Scattering Model (ASM). Unlike conventional approaches, our method utilizes ASM to initialize reflectance estimation by adaptively adjusting atmospheric light and transmittance. A weighted graph is then employed to effectively handle local brightness variation. Additionally, a regularization term is introduced to suppress noise, refine reflectance estimation, and maintain balanced brightness enhancement. Extensive experiments on multiple benchmark remote sensing datasets demonstrate that our approach outperforms state-of-the-art methods, delivering superior enhancement performance and visual quality, even under complex non-uniform low-light conditions.

The problem of blind image deblurring remains a challenging inverse problem, due to the ill-posed nature of estimating unknown blur kernels and latent images within the Maximum A Posteriori (MAP) framework. To address this challenge, traditional methods often rely on sparse regularization priors to mitigate the uncertainty inherent in the problem. In this paper, we propose a novel blind deblurring model based on the MAP framework that leverages Composite-Gradient Feature (CGF) variations in edge regions after image blurring. This prior term is specifically designed to exploit the high sparsity of sharp edge regions in clear images, thereby effectively alleviating the ill-posedness of the problem. Unlike existing methods that focus on local gradient information, our approach focuses on the aggregation of edge regions, enabling better detection of both sharp and smoothed edges in blurred images. In the blur kernel estimation process, we enhance the accuracy of the kernel by assigning effective edge information from the blurred image to the smoothed intermediate latent image, preserving critical structural details lost during the blurring process. To further improve the edge-preserving restoration, we introduce an adaptive regularizer that outperforms traditional total variation regularization by better maintaining edge integrity in both clear and blurred images. The proposed variational model is efficiently implemented using alternating iterative techniques. Extensive numerical experiments and comparisons with state-of-the-art methods demonstrate the superior performance of our approach, highlighting its effectiveness and real-world applicability in diverse image-restoration tasks.

The non-uniformity of infrared detectors’ readout circuits can lead to stripe noise in infrared images, which affects their effective information and poses challenges for subsequent applications. Traditional denoising algorithms have limited effectiveness in maintaining effective information. This paper proposes a multi-level image decomposition method based on an improved LatLRR (MIDILatLRR). By utilizing the global low-rank structural characteristics of stripe noise, the noise and smooth information are decomposed into low-rank part images, and texture information is adaptively decomposed into several salient part images, thereby better preserving texture edge information in the image. Sparse terms are constructed according to the smoothness of the effective information in the final low-rank part of the image and the sparsity of the stripe noise direction. The modeling of stripe noise is achieved using multi-sparse constraint representation (MSCR), and the Alternating Direction Method of Multipliers (ADMM) is used for calculation. Extensive experiments demonstrated the proposed algorithm’s effectiveness and compared it with state-of-the-art algorithms in subjective judgments and objective indicators. The experimental results fully demonstrate the proposed algorithm’s superiority and efficacy.