RSINet: Inpainting Remotely Sensed Images Using Triple GAN Framework

Kumar, Advait; Tamboli, Dipesh; Pande, Shivam; Banerjee, Biplab

doi:10.1109/igarss46834.2022.9884330

Cited by 1 publication

(1 citation statement)

References 5 publications

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“…Goodfellow proposed the Generative Adversarial Network (GAN), which employs two convolutional neural networks in a game-based training approach to generate images resembling the original picture ( Ozkanoglu & Ozer, 2022 ; He, Wandt & Rhodin, 2022 ; Iqbal & Ali, 2018 ; Kumar et al, 2022 ; Zhao, Wei & Wong, 2022 ). The GAN model consists of two essential components: the generator and the discriminator.…”

Section: Generating Adversarial Networkmentioning

confidence: 99%

A 2D image 3D reconstruction function adaptive denoising algorithm

Wang,

Ni,

Liu

et al. 2023

PeerJ Computer Science

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To address the issue of image denoising algorithms blurring image details during the denoising process, we propose an adaptive denoising algorithm for the 3D reconstruction of 2D images. This algorithm takes into account the inherent visual characteristics of human eyes and divides the image into regions based on the entropy value of each region. The background region is subject to threshold denoising, while the target region undergoes processing using an adversarial generative network. This network effectively handles 2D target images with noise and generates a 3D model of the target. The proposed algorithm aims to enhance the noise immunity of 2D images during the 3D reconstruction process and ensure that the constructed 3D target model better preserves the original image’s detailed information. Through experimental testing on 2D images and real pedestrian videos contaminated with noise, our algorithm demonstrates stable preservation of image details. The reconstruction effect is evaluated in terms of noise reduction and the fidelity of the 3D model to the original target. The results show an average noise reduction exceeding 95% while effectively retaining most of the target’s feature information in the original image. In summary, our proposed adaptive denoising algorithm improves the 3D reconstruction process by preserving image details that are often compromised by conventional denoising techniques. This has significant implications for enhancing image quality and maintaining target information fidelity in 3D models, providing a promising approach for addressing the challenges associated with noise reduction in 2D images during 3D reconstruction.

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