369 research outputs found
Exploring Dehazing Methods For Remote Sensing Imagery: A Review
Remote sensing imagery plays a pivotal role in numerous applications, from environmental monitoring to disaster management. However, the occurrence of haze which is atmospheric often reduces the quality and interpretability of these images. Atmospheric Haze reduces visibility of remote sensed images by reducing contrast and causing colour distortions. Dehazing techniques are employed to improve the perceptibility and clarity affected images by haze. In this review, we delve into the realm of dehazing methods specifically tailored for remote sensing imagery, aiming to shed light on their efficacy and applicability. We focus on a comprehensive comparison of four prominent dehazing techniques: Histogram Equalization (HE), Light Channel Prior (LCP), Contrast Enhancement Filters (CEF), and Dark Channel Prior (DCP). These methods, representing a spectrum of approaches, are evaluated based on key quality metrics of images, including PSNR, MSE and SSIM
A Review of Remote Sensing Image Dehazing.
Remote sensing (RS) is one of the data collection technologies that help explore more earth surface information. However, RS data captured by satellite are susceptible to particles suspended during the imaging process, especially for data with visible light band. To make up for such deficiency, numerous dehazing work and efforts have been made recently, whose strategy is to directly restore single hazy data without the need for using any extra information. In this paper, we first classify the current available algorithm into three categories, i.e., image enhancement, physical dehazing, and data-driven. The advantages and disadvantages of each type of algorithm are then summarized in detail. Finally, the evaluation indicators used to rank the recovery performance and the application scenario of the RS data haze removal technique are discussed, respectively. In addition, some common deficiencies of current available methods and future research focus are elaborated
Vision Transformers for Single Image Dehazing
Image dehazing is a representative low-level vision task that estimates
latent haze-free images from hazy images. In recent years, convolutional neural
network-based methods have dominated image dehazing. However, vision
Transformers, which has recently made a breakthrough in high-level vision
tasks, has not brought new dimensions to image dehazing. We start with the
popular Swin Transformer and find that several of its key designs are
unsuitable for image dehazing. To this end, we propose DehazeFormer, which
consists of various improvements, such as the modified normalization layer,
activation function, and spatial information aggregation scheme. We train
multiple variants of DehazeFormer on various datasets to demonstrate its
effectiveness. Specifically, on the most frequently used SOTS indoor set, our
small model outperforms FFA-Net with only 25% #Param and 5% computational cost.
To the best of our knowledge, our large model is the first method with the PSNR
over 40 dB on the SOTS indoor set, dramatically outperforming the previous
state-of-the-art methods. We also collect a large-scale realistic remote
sensing dehazing dataset for evaluating the method's capability to remove
highly non-homogeneous haze
Fast Deep Multi-patch Hierarchical Network for Nonhomogeneous Image Dehazing
Recently, CNN based end-to-end deep learning methods achieve superiority in
Image Dehazing but they tend to fail drastically in Non-homogeneous dehazing.
Apart from that, existing popular Multi-scale approaches are runtime intensive
and memory inefficient. In this context, we proposed a fast Deep Multi-patch
Hierarchical Network to restore Non-homogeneous hazed images by aggregating
features from multiple image patches from different spatial sections of the
hazed image with fewer number of network parameters. Our proposed method is
quite robust for different environments with various density of the haze or fog
in the scene and very lightweight as the total size of the model is around 21.7
MB. It also provides faster runtime compared to current multi-scale methods
with an average runtime of 0.0145s to process 1200x1600 HD quality image.
Finally, we show the superiority of this network on Dense Haze Removal to other
state-of-the-art models.Comment: CVPR Workshops Proceedings 202
Rich Feature Distillation with Feature Affinity Module for Efficient Image Dehazing
Single-image haze removal is a long-standing hurdle for computer vision
applications. Several works have been focused on transferring advances from
image classification, detection, and segmentation to the niche of image
dehazing, primarily focusing on contrastive learning and knowledge
distillation. However, these approaches prove computationally expensive,
raising concern regarding their applicability to on-the-edge use-cases. This
work introduces a simple, lightweight, and efficient framework for single-image
haze removal, exploiting rich "dark-knowledge" information from a lightweight
pre-trained super-resolution model via the notion of heterogeneous knowledge
distillation. We designed a feature affinity module to maximize the flow of
rich feature semantics from the super-resolution teacher to the student
dehazing network. In order to evaluate the efficacy of our proposed framework,
its performance as a plug-and-play setup to a baseline model is examined. Our
experiments are carried out on the RESIDE-Standard dataset to demonstrate the
robustness of our framework to the synthetic and real-world domains. The
extensive qualitative and quantitative results provided establish the
effectiveness of the framework, achieving gains of upto 15\% (PSNR) while
reducing the model size by 20 times.Comment: Preprint version. Accepted at Opti
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