Luminance Learning for Remotely Sensed Image Enhancement Guided by Weighted Least Squares

Huang, Zhenghua; Zhu, Zifan; An, Qing; Wang, Zhicheng; Zhou, Qin

doi:10.1109/lgrs.2021.3093935

Cited by 17 publications

(5 citation statements)

References 30 publications

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“…Quantitative index selection: Except for subjective evaluation, we, in the simulated experiments, employed the peak-signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) to objectively assess the capacity of the state-of-the-art in AWGN removal and structural preservation, respectively [73]. In real-world experiments, two reference-free metrics, the Q-metric (QM) [4] and natural image quality evaluator (NIQE) [86], were used to evaluate their ability to preserve fine structures and improve estimated image quality, respectively. The higher the PSNR is, the better the AWGN noise suppression.…”

Section: Experimental Preparationmentioning

confidence: 99%

MD3: Model-Driven Deep Remotely Sensed Image Denoising

et al. 2023

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Remotely sensed images degraded by additive white Gaussian noise (AWGN) have low-level vision, resulting in a poor analysis of their contents. To reduce AWGN, two types of denoising strategies, sparse-coding-model-based and deep-neural-network-based (DNN), are commonly utilized, which have their respective merits and drawbacks. For example, the former pursue enjoyable performance with a high computational burden, while the latter have powerful capacity in completing a specified task efficiently, but this limits their application range. To combine their merits for improving performance efficiently, this paper proposes a model-driven deep denoising (MD3) scheme. To solve the MD3 model, we first decomposed it into several subproblems by the alternating direction method of multipliers (ADMM). Then, the denoising subproblems are replaced by different learnable denoisers, which are plugged into the unfolded MD3 model to efficiently produce a stable solution. Both quantitative and qualitative results validate that the proposed MD3 approach is effective and efficient, while it has a more powerful ability in generating enjoyable denoising performance and preserving rich textures than other advanced methods.

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Section: Experimental Preparationmentioning

confidence: 99%

MD3: Model-Driven Deep Remotely Sensed Image Denoising

et al. 2023

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“…However, a DCN has a new convolutional layer and a fully connected layer for learning offsets. However, owing to the finite adaptive learning ability of the convolution kernel [ 25 ], DCNsv1 still contains content unrelated to the image content. The advantage of DCNs over CNNs lies in their ability to adapt to geometric changes in objects.…”

Section: Related Studiesmentioning

confidence: 99%

Robotic Grasp Detection Network Based on Improved Deformable Convolution and Spatial Feature Center Mechanism

Zou,

Li,

Yuan

et al. 2023

Biomimetics

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In this article, we propose an effective grasp detection network based on an improved deformable convolution and spatial feature center mechanism (DCSFC-Grasp) to precisely grasp unidentified objects. DCSFC-Grasp includes three key procedures as follows. First, improved deformable convolution is introduced to adaptively adjust receptive fields for multiscale feature information extraction. Then, an efficient spatial feature center (SFC) layer is explored to capture the global remote dependencies through a lightweight multilayer perceptron (MLP) architecture. Furthermore, a learnable feature center (LFC) mechanism is reported to gather local regional features and preserve the local corner region. Finally, a lightweight CARAFE operator is developed to upsample the features. Experimental results show that DCSFC-Grasp achieves a high accuracy (99.3% and 96.1% for the Cornell and Jacquard grasp datasets, respectively) and even outperforms the existing state-of-the-art grasp detection models. The results of real-world experiments on the six-DoF Realman RM65 robotic arm further demonstrate that our DCSFC-Grasp is effective and robust for the grasping of unknown targets.

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“…Transform-based methods always produce over-estimation results when noise levels are low [ 38 , 39 ]. Patch-based methods may generate underestimation results at high noise levels [ 17 , 40 , 41 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Blind Additive Gaussian White Noise Level Estimation from a Single Image by Employing Chi-Square Distribution

Wang

Zhu

et al. 2022

Entropy

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The additive Gaussian white noise (AGWN) level in real-life images is usually unknown, for which the empirical setting will make the denoising methods over-smooth fine structures or remove noise incompletely. The previous noise level estimation methods are easily lost in accurately estimating them from images with complicated structures. To cope with this issue, we propose a novel noise level estimation scheme based on Chi-square distribution, including the following key points: First, a degraded image is divided into many image patches through a sliding window. Then, flat patches are selected by using a patch selection strategy on the gradient maps of those image patches. Next, the initial noise level is calculated by employing Chi-square distribution on the selected flat patches. Finally, the stable noise level is optimized by an iterative strategy. Quantitative, with association, to qualitative results of experiments on synthetic real-life images validate that the proposed noise level estimation method is effective and even superior to the state-of-the-art methods. Extensive experiments on noise removal using BM3D further illustrate that the proposed noise level estimation method is more beneficial for achieving favorable denoising performance with detail preservation.

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Luminance Learning for Remotely Sensed Image Enhancement Guided by Weighted Least Squares

Cited by 17 publications

References 30 publications

MD3: Model-Driven Deep Remotely Sensed Image Denoising

MD3: Model-Driven Deep Remotely Sensed Image Denoising

Robotic Grasp Detection Network Based on Improved Deformable Convolution and Spatial Feature Center Mechanism

Blind Additive Gaussian White Noise Level Estimation from a Single Image by Employing Chi-Square Distribution

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