LWCNN: a lightweight convolutional neural network for agricultural crop protection

Raman, Sundaresan; Soni, Manan; Ramaprasad, Rohit; Chamola, Vinay

doi:10.1007/s11042-021-11866-0

Cited by 5 publications

(1 citation statement)

References 22 publications

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“…A number of these applications use cameras and recording devices that produce low‐resolution (LR) images and low‐quality footage due to the devices being low‐cost and using cheap equipment. Super‐resolution imaging techniques have been employed in a large number of possible use cases, including satellite (Kuter et al, 2014a; Kuter et al, 2014b; Tekeli et al, 2005; Xu et al, 2023; Xu Pan, et al, 2022) and aerial imagery (Arun et al, 2020; Chakravarthy et al, 2022; Guo ret al, 2022; Jain et al, 2021; Lin et al, 2022; Raman, Soni, et al, 2022; Wang et al, 2022), intelligence monitoring, medical image processing, and fingerprint enhancement. The pixel density inside an image is significant in high resolution.…”

Section: Introductionmentioning

confidence: 99%

A novel generative adversarial network‐based super‐resolution approach for face recognition

Chougule,

Kolte,

Chamola

et al. 2024

Expert Systems

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Face recognition is an essential feature required for a range of computer vision applications such as security, attendance systems, emotion detection, airport check‐in, and many others. The super‐resolution of subject images is an important and challenging element in numerous scenarios. At times the images are low resolution and need to be processed through super‐resolution techniques to gain more accurate results. For the problem of image super‐resolution, deep learning‐based face recognition systems have been explored in recent years; however, low‐resolution face recognition remains an arduous task. Generative adversarial network (GAN) based models are a promising approach to address this challenge. However, conventional GAN‐based models may generate images that differ significantly from an original high‐resolution image in the test set to the point that the identity of the target face may be changed. To address this shortcoming, we propose a novel U‐Net style generator architecture, where skip‐connections between the encoder and decoder layer can help in preserving the facial characteristics of the input image in the generated image, thus curbing the generator's ability to generate an entirely new image and training it to generate an image more similar in characteristics to the original image. In addition to statistical metrics like structural similarity index measure and Fréchet inception distance, we compute the pixel‐wise distance between the original and model‐generated images to ascertain that our model generates as close to the original images as possible. While we train the model for 4× super‐resolution (64 × 64 images to 256 × 256), our architecture can also be trained for an arbitrary resizing scale. Finally, the number of faces detected over high‐resolution images generated by our model is shown to be higher than state‐of‐the‐art high‐resolution image creation models for face recognition tasks.

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