Cloud Removal from Aerial Images Using Generative Adversarial Network with Simple Image Enhancement

Pacot, Mark Phil B.; Marcos, Nelson

doi:10.1145/3383812.3383838

Cited by 3 publications

(1 citation statement)

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“…In this article, perception based image quality evaluator (PIQE), a representative evaluation method, is applied. As a built-in function in the image processing toolbox of MATLAB, the quality evaluation ability and impartiality of PIQE have been proven, and it has been widely used in the evaluation tasks of images generated by deep learning model, especially in generative adversarial networks, such as [20][21][22] et al For urban-wise UAV applications, high authenticity and avoidance of distortion and degradation are required by the subsequent intelligence image algorithms. Meanwhile, in each patch of urban UAV aerial photography captured image, items are greatly complex and various.…”

Section: Image Quality Assessment (Nr-iqa)mentioning

confidence: 99%

LighterGAN: An Illumination Enhancement Method for Urban UAV Imagery

Wang

Chen

et al. 2021

Remote Sensing

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In unmanned aerial vehicle based urban observation and monitoring, the performance of computer vision algorithms is inevitably limited by the low illumination and light pollution caused degradation, therefore, the application image enhancement is a considerable prerequisite for the performance of subsequent image processing algorithms. Therefore, we proposed a deep learning and generative adversarial network based model for UAV low illumination image enhancement, named LighterGAN. The design of LighterGAN refers to the CycleGAN model with two improvements—attention mechanism and semantic consistency loss—having been proposed to the original structure. Additionally, an unpaired dataset that was captured by urban UAV aerial photography has been used to train this unsupervised learning model. Furthermore, in order to explore the advantages of the improvements, both the performance in the illumination enhancement task and the generalization ability improvement of LighterGAN were proven in the comparative experiments combining subjective and objective evaluations. In the experiments with five cutting edge image enhancement algorithms, in the test set, LighterGAN achieved the best results in both visual perception and PIQE (perception based image quality evaluator, a MATLAB build-in function, the lower the score, the higher the image quality) score of enhanced images, scores were 4.91 and 11.75 respectively, better than EnlightenGAN the state-of-the-art. In the enhancement of low illumination sub-dataset Y (containing 2000 images), LighterGAN also achieved the lowest PIQE score of 12.37, 2.85 points lower than second place. Moreover, compared with the CycleGAN, the improvement of generalization ability was also demonstrated. In the test set generated images, LighterGAN was 6.66 percent higher than CycleGAN in subjective authenticity assessment and 3.84 lower in PIQE score, meanwhile, in the whole dataset generated images, the PIQE score of LighterGAN is 11.67, 4.86 lower than CycleGAN.

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Section: Image Quality Assessment (Nr-iqa)mentioning

confidence: 99%