Learning a Contrast Enhancer for Intensity Correction of Remotely Sensed Images

Huang, Zhenghua; Wang, Lei; An, Qing; Zhou, Qin; Hu, Hong

doi:10.1109/lsp.2021.3138351

Cited by 22 publications

(10 citation statements)

References 29 publications

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“…However, their differences at each level on each image are small, resulting in the subtle differences of visual results, especially in the enlarged areas. The quantitative denoising results (the peak signal-to-noise ratio (PSNR) [ 44 ] is adopted to assess their ability for noise reduction and the structural similarity index (SSIM) [ 45 ] is exploited to assess their capability for structure preservation) are respectively presented in Table 2 and Table 3 . We combine BM3D with true noise level to obtain the reconstructed image and use its PSNR and SSIM values as benchmarks to evaluate noise level estimation methods.…”

Section: Resultsmentioning

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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Section: Resultsmentioning

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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“…So, they suggested that these studies could be used in drones. Huang et al (2021aHuang et al ( , 2021b introduced a global-local enhancement network that decomposes images using discrete wavelet transformation and enhances them separately, improving brightness and detail preservation. Also proposed a luminance learning framework guided by weighted least squares, which separated images into base and detail layers and enhanced them using a learning network and an enhancement operator, respectively.…”

Section: Discussionmentioning

confidence: 99%

Design and implementation of an AI-controlled spraying drone for agricultural applications using advanced image preprocessing techniques

Akdoğan,

Özer,

Oğuz

2024

RIA

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Purpose Nowadays, food problems are likely to arise because of the increasing global population and decreasing arable land. Therefore, it is necessary to increase the yield of agricultural products. Pesticides can be used to improve agricultural land products. This study aims to make the spraying of cherry trees more effective and efficient with the designed artificial intelligence (AI)-based agricultural unmanned aerial vehicle (UAV). Design/methodology/approach Two approaches have been adopted for the AI-based detection of cherry trees: In approach 1, YOLOv5, YOLOv7 and YOLOv8 models are trained with 70, 100 and 150 epochs. In Approach 2, a new method is proposed to improve the performance metrics obtained in Approach 1. Gaussian, wavelet transform (WT) and Histogram Equalization (HE) preprocessing techniques were applied to the generated data set in Approach 2. The best-performing models in Approach 1 and Approach 2 were used in the real-time test application with the developed agricultural UAV. Findings In Approach 1, the best F1 score was 98% in 100 epochs with the YOLOv5s model. In Approach 2, the best F1 score and mAP values were obtained as 98.6% and 98.9% in 150 epochs, with the YOLOv5m model with an improvement of 0.6% in the F1 score. In real-time tests, the AI-based spraying drone system detected and sprayed cherry trees with an accuracy of 66% in Approach 1 and 77% in Approach 2. It was revealed that the use of pesticides could be reduced by 53% and the energy consumption of the spraying system by 47%. Originality/value An original data set was created by designing an agricultural drone to detect and spray cherry trees using AI. YOLOv5, YOLOv7 and YOLOv8 models were used to detect and classify cherry trees. The results of the performance metrics of the models are compared. In Approach 2, a method including HE, Gaussian and WT is proposed, and the performance metrics are improved. The effect of the proposed method in a real-time experimental application is thoroughly analyzed.

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“…Note that hyperspectral images were treated as common RSI images in a band-by-band manner to be proceeded. The Adam optimizer [83,84] was used to minimize the L 1 loss function, which was adopted to generate the optimization network parameters shared in each stage. The block size was 8 × 8, while the dictionary size was 256 × 256.…”

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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Learning a Contrast Enhancer for Intensity Correction of Remotely Sensed Images

Cited by 22 publications

References 29 publications

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

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

Design and implementation of an AI-controlled spraying drone for agricultural applications using advanced image preprocessing techniques

MD3: Model-Driven Deep Remotely Sensed Image Denoising

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