Remote Sensing Image Dehazing Using Heterogeneous Atmospheric Light Prior

Abstract

Remote sensing images (RSIs) captured in haze weather will suffer from serious quality degradation with color distortion and contrast reduction, which creates numerous challenges for the utilization of RSIs. To address these issues, this paper proposes a novel haze removal algorithm, named HALP, for visible RSIs based on a heterogeneous atmospheric light prior and side window filter. HALP is comprised of two key components. Firstly, given the large imaging space of RSIs, the atmospheric light is treated as a globally non-uniform distribution instead of a global constant. Therefore, a simple and effective method for non-uniform atmospheric light estimation is presented, which utilizes the brightest pixel color in each local image patch as the atmospheric light of the local region. Secondly, a side window filter-based transmission estimation algorithm is proposed, which can effectively suppress the block effect in the transmission map caused by the large window of the minimum filter used in the dark channel algorithm. Experiments on both real-world and synthetic remote sensing haze images demonstrate the effectiveness of HALP. In terms of no-reference and full-reference image quality assessments, HALP yields excellent results, outperforming existing state-of-the-art algorithms, including physics-based and neural network-based methods. The visual comparison of dehazed results also shows that HALP can restore degraded RSIs with uneven haze, producing clear images with rich details and natural colors.INDEX TERMS Dehazing, remote sensing image, heterogeneous atmospheric light, image restoration, dark channel.

Advancements in Remote Sensing Image Dehazing: Introducing URA-Net with Multi-Scale Dense Feature Fusion Clusters and Gated Jump Connection

“…It is imperative to acknowledge that the diminished intensity observed in the dark channel primarily stems from shadows cast by the scene, the presence of dark objects, and vividly colored surfaces or objects. The dark channel a priori defogging algorithm, grounded in statistical principles, has demonstrated commendable outcomes within the realm of image defogging, exhibiting greater stability in comparison to the aforementioned non-physical model algorithms [34][35][36][37][38]. This algorithm can more accurately estimate the thickness of the fog, resulting in a more natural and clearer defogging effect [39][40][41].…”

GLUENet: An Efficient Network for Remote Sensing Image Dehazing with Gated Linear Units and Efficient Channel Attention

“…For the purpose of dehazing aerial images, Kulkarni et al [24] suggested a novel deformable multi-head attention mechanism with a spatial attention offset extraction solution. For visible light Remote Sensing Imagery (RSI), He et al [25] presented a unique haze removal algorithm known as HALP based on heterogeneous atmospheric light prior and side window filtering. An algorithm for dehazing visible light remote sensing images, called SRD, was presented by He et al [26].…”

Remote Sensing Image Haze Removal Based on Superpixel

“…To validate the dehazing ability of the SRD algorithm on remote sensing images, we compare and analyze the haze removal results of SRD and other state-of-the-art methods on real-world haze images. All test samples are from the Real-world Remote Sensing Haze Image Dataset (RRSHID) [44], which contains 277 degraded images with haze, all of which are manually selected by the authors of RRSHID from two widely used remote sensing datasets, AID [45] and DIOR [46]. In this experiment, we employ three types of test samples for a more comprehensive assessment: (1) challenging samples with dense haze; (2) images with different color distributions; and (3) remote sensing haze images of various scenes.…”

“…Here, we comprehensively compare the average processing time of various dehazing algorithms for input images with different resolutions. We randomly select 100 images from the RRSHID dataset [44] mentioned in Section 4.2 and resize them to three resolutions: 256 × 256, 512 × 512, and 1024 × 1024, constituting three test sets. Then, we execute the SRD algorithm and each comparison method separately on the same computer (described in Section 4.1) and calculate their average computing time on these three test sets.…”