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Deep Learning for Remote Sensing Image Enhancement

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Dear Colleagues,

Enhancing images is a challenging task due to several factors, including color inconsistencies, low contrast, blurriness, low signal-to-noise ratios, and uneven lighting. Recent progress in deep learning has offered solutions to tackle some of these issues. Nevertheless, given the complex nature of imaging, certain technical hurdles remain. These include the scarcity of adequately labeled training data and the ability of deep learning algorithms to not only enhance images but to also provide useful information on downstream tasks (such as object detection and segmentation).

The aim of this Special Issue is to provide a forum for cutting-edge research works that address the ongoing challenges in image enhancement using state-of-the-art deep learning methods. Please note that images/data need to be acquired by remote sensing methods We welcome topics that include, but are not limited to, the following:

Image color balancing;
Generative deep learning for image generation;
Image quality assessment using reference or no-reference evaluation metrics;
Image super-resolution;
Image denoising;
New image datasets.

Dr. Sidike Paheding
Dr. Ashraf Saleem
Guest Editors

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

image enhancement
deep learning
underwater images
image restoration
Image database

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19 pages, 18847 KiB

Open AccessArticle

Remote Sensing Image Dehazing via a Local Context-Enriched Transformer

by Jing Nie, Jin Xie and Hanqing Sun

Remote Sens. 2024, 16(8), 1422; https://doi.org/10.3390/rs16081422 - 17 Apr 2024

Viewed by 335

Abstract

Remote sensing image dehazing is a well-known remote sensing image processing task focused on restoring clean images from hazy images. The Transformer network, based on the self-attention mechanism, has demonstrated remarkable advantages in various image restoration tasks, due to its capacity to capture long-range dependencies within images. However, it is weak at modeling local context. Conversely, convolutional neural networks (CNNs) are adept at capturing local contextual information. Local contextual information could provide more details, while long-range dependencies capture global structure information. The combination of long-range dependencies and local context modeling is beneficial for remote sensing image dehazing. Therefore, in this paper, we propose a CNN-based adaptive local context enrichment module (ALCEM) to extract contextual information within local regions. Subsequently, we integrate our proposed ALCEM into the multi-head self-attention and feed-forward network of the Transformer, constructing a novel locally enhanced attention (LEA) and a local continuous-enhancement feed-forward network (LCFN). The LEA utilizes the ALCEM to inject local context information that is complementary to the long-range relationship modeled by multi-head self-attention, which is beneficial to removing haze and restoring details. The LCFN extracts multi-scale spatial information and selectively fuses them by the the ALCEM, which supplements more informative information compared with existing regular feed-forward networks with only position-specific information flow. Powered by the LEA and LCFN, a novel Transformer-based dehazing network termed LCEFormer is proposed to restore clear images from hazy remote sensing images, which combines the advantages of CNN and Transformer. Experiments conducted on three distinct datasets, namely DHID, ERICE, and RSID, demonstrate that our proposed LCEFormer achieves the state-of-the-art performance in hazy scenes. Specifically, our LCEFormer outperforms DCIL by 0.78 dB and 0.018 for PSNR and SSIM on the DHID dataset. Full article

(This article belongs to the Special Issue Deep Learning for Remote Sensing Image Enhancement)

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