Compared-neighborhood based image dehazing for improved visibility

Alenezi, Fayadh

doi:10.1016/j.engappai.2023.106001

Cited by 3 publications

(1 citation statement)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…6(i), lack depth and clarity, whereas in fig. 6(j), Ali et al [56] produced images that are underexposed. In contrast, as depicted in Fig.…”

Section: ) Qualitative Analysismentioning

confidence: 99%

SARain-GAN: Spatial Attention Residual UNet Based Conditional Generative Adversarial Network for Rain Streak Removal

Kolekar,

Bose,

Pai

2024

IEEE Access

View full text Add to dashboard Cite

Deraining of images plays a pivotal role in computer vision by addressing the challenges posed by rain, enhancing visibility, and refining image quality by eliminating rain streaks. Traditional methods often fall short of effectively handling intricate rain patterns, resulting in incomplete removal. In this paper, we propose an innovative deep learning-based deraining model leveraging a modified residual UNet and a multiscale attention-guided convolutional neural network module as a discriminator within a conditional generative adversarial network framework. The proposed approach introduces custom hyperparameters and a tailored loss function to facilitate the efficient removal of rain streaks from images. Evaluation on both synthetic and real-world datasets showcases superior performance, as indicated by improved image evaluation metrics such as PSNR, SSIM, and NIQE. The effectiveness of our model extends to improving both rainy and foggy images. We also conducted a comparative analysis of computational complexity in terms of running time, GFLOPs, and no. of parameters against other state-of-the-art methods to demonstrate our model's superiority.

show abstract

“…6(i), lack depth and clarity, whereas in fig. 6(j), Ali et al [56] produced images that are underexposed. In contrast, as depicted in Fig.…”

Section: ) Qualitative Analysismentioning

confidence: 99%