MAGAN: Unsupervised low-light image enhancement guided by mixed-attention

Wang, Renjun; Jiang, Bin; Yang, Chao; Qiao, Liyan; Zhang, Bolin

doi:10.26599/bdma.2021.9020020

Cited by 38 publications

(13 citation statements)

References 37 publications

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“…Wang et al said that China has a wide variety of remote sensing satellites and a complete system. In recent years, my country has increased the use of high-precision remote sensing satellites, reaching the highest level in the world [4]. e panchromatic image resolution of "Gaogao-1" launched in April 2013 can reach 2 m and can also provide 8 m spatial resolution multispectral images with combined width better than 60 km or 16 m spatial resolution multispectral images with combined width better than 800 km.…”

Section: Literature Reviewmentioning

confidence: 99%

High-Precision Target Detection of Remote Sensing Image Based on Feature Enhancement with 6G Technology

Chen

2022

Advances in Multimedia

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We provide remote sensing image enhancement technology based on 6G nursing urine technology to solve the main problems and current challenges of target detection in remote sensing imaging. High-resolution images: to get a good image, first look at the remote control image, and objective findings on the remote control image play an important role in this process. Military and civilian use: at present, many advanced object detection algorithms have achieved success in natural imaging, but their development is limited by two factors: large variation in object size and low detection accuracy, the scale, rotation direction, and distribution density of remote sensing images. On the other hand, the first remote sensing image is usually a high-resolution, large-scale image, which requires more time than blank image detection. The model is fine-tuned on the ICDAR2015 training package for 60,000 repetitions. The same model experimentally reduced the image to 1280 × 768 and displayed the results in one dimension. Therefore, in order to solve the problem of low detection due to changes in azimuth, rotation direction, and velocity distribution, it is necessary to further study methods for detecting large-capacity remote sensing objects using abundant high-precision remote sensing technologies, sensing imaging service on remote sensing maps.

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Section: Literature Reviewmentioning

confidence: 99%

High-Precision Target Detection of Remote Sensing Image Based on Feature Enhancement with 6G Technology

Chen

2022

Advances in Multimedia

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“…In order to ensure the same amplitude of image enhancement, it is necessary to enhance the illumination of all pixels in the image [27,28]; that is, first extract the brightness component of the original image, that is, the V component in the HSV color space, and then generate the brightness gain curve kðx, yÞ. The problem of extracting the brightness component is the maximum value processing of RGB three channels; that is, Vðx, yÞ = max fRðx, yÞ, Gðx, yÞ, Bðx, yÞg.…”

Section: Mural Digital Image Enhancementmentioning

confidence: 99%

Intelligent Restoration Technology of Mural Digital Image Based on Machine Learning Algorithm

Zhou

Xie

2022

Wireless Communications and Mobile Computing

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Influenced by the natural environment and preservation conditions, the frescoes in the monastery were damaged to some extent. In order to solve the fresco image restoration conversion and structural decomposition defects, an intelligent fresco digital image restoration technique based on machine learning algorithm is proposed. Firstly, the digital image information of mural is collected by a scanning program, and the data in the database is extracted. The mean filter template is used to restore the color of mural digital image, and the Gaussian template is used to restore the color of image details. The two are superimposed to ensure that the image is not affected by distortion and halo, so as to ensure the clarity of image and boundary. The depth learning model of local fuzzy feature restoration of mural digital image is obtained by using multimodal feature decomposition method, and the intelligent restoration of mural digital image is realized by this model. The experimental results show that this method can effectively repair the local fuzzy features of mural digital image. The repair precision is more than 95.7% and fills in the missing information from the image, and the image quality is good.

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“…Existing enhancement methods can be divided into two categories. (1) Traditional methods [1][2] [3], these methods depend on carefully designed parameters and cannot be extended to various lighting conditions; (2) Deep learning method, in which [4] [5][6] [7] needs a large number of paired low-/normal-light images for training, [8] [9][10] [11] does not need a paired dataset, but needs to carefully select a large number of unpaired normal-light images to train the model. Although these methods have achieved promising results, it is difficult to obtain a dataset.…”

Section: Introductionmentioning

confidence: 99%

Retinex-SIE: self-supervised low-light image enhancement method based on Retinex and homomorphic filtering transformation

Yang¹,

Qian²,

Liu³

2023

Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)

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Low-light images suffer from low visibility, much noise, uneven illumination distribution, etc. Many existing methods have problems such as over enhancement or insufficient detail enhancement when dealing with low-light images with uneven illumination distribution. To remedy the above shortcomings, we proposes a Retinex-based self-supervised lowlight image enhancement model (Retinex-SIE), which is mainly composed of three parts: Retinex-based self-fsupervised image decomposition network (Retinex-DNet), nonlinear conditional illumination enhancement function (NCIEF), and image reconstruction (IR). First, a uniform illumination image of the same scene with the low-light image is generated by homomorphic filtering transformation, and the low-light image and the uniform illumination image are input into Retinex-DNet for decomposition to obtain reflectivity, noise and illumination. Then, NCIEF is used to enhance the illumination after decomposition. Finally, the final enhanced image is obtained by multiplying the decomposed reflectance and the enhanced illumination. Experiments on severa challenging low-light image datasets show that Retinex-SIE proposed in this paper can better handle low-light images with uneven illumination distribution, and avoid problems such as excessive enhancement or insufficient detail enhancement.

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MAGAN: Unsupervised low-light image enhancement guided by mixed-attention

Cited by 38 publications

References 37 publications

High-Precision Target Detection of Remote Sensing Image Based on Feature Enhancement with 6G Technology

High-Precision Target Detection of Remote Sensing Image Based on Feature Enhancement with 6G Technology

Intelligent Restoration Technology of Mural Digital Image Based on Machine Learning Algorithm

Retinex-SIE: self-supervised low-light image enhancement method based on Retinex and homomorphic filtering transformation

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