An Image Enhancement Method for Autonomous Vehicles Driving in Poor Visibility Circumstances

Duong, Minh-Thien; Phan, Thanh-Danh; Truong, Nghe-Nhan; Le, Manh Cuong; Do, Truong-Dong; Nguyen, Vanthuan; Le, My-Ha

doi:10.1007/978-3-031-19694-2_2

Cited by 2 publications

(2 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…MOTIVATION I MAGE enhancement involves manipulating an original image to enhance specific features or characteristics, with the objective of improving its visual quality by mitigating factors that may degrade its appearance. Various illumination conditions like low-light [1] [2] and backlit [3] as well as weather advarsities [4] like rain, snow, and haze negatively affect the quality and visibility of an image. Deraining of images plays a pivotal role in computer vision by addressing the challenges posed by rain, enhancing visibility, and refining image quality [5].…”

Section: Introductionmentioning

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

Section: Introductionmentioning

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

show abstract

“…Secondly, objects are recognized using the convolutional neural network (CNN) [13]. Another example is [14], where images are pre-processed with Gaussian blur. The simulation of a self-driving car has been carried out, which can learn to drive autonomously without manual intervention of human beings using deep CNN.…”

Section: Introductionmentioning

confidence: 99%

Impact of Traditional and Embedded Image Denoising on CNN-Based Deep Learning

Kaur,

Karmakar,

Imran

2023

Applied Sciences

View full text Add to dashboard Cite

In digital image processing, filtering noise is an important step for reconstructing a high-quality image for further processing such as object segmentation, object detection, and object recognition. Various image-denoising approaches, including median, Gaussian, and bilateral filters, are available in the literature. Since convolutional neural networks (CNN) are able to directly learn complex patterns and features from data, they have become a popular choice for image-denoising tasks. As a result of their ability to learn and adapt to various denoising scenarios, CNNs are powerful tools for image denoising. Some deep learning techniques such as CNN incorporate denoising strategies directly into the CNN model layers. A primary limitation of these methods is their necessity to resize images to a consistent size. This resizing can result in a loss of vital image details, which might compromise CNN’s effectiveness. Because of this issue, we utilize a traditional denoising method as a preliminary step for noise reduction before applying CNN. To our knowledge, a comparative performance study of CNN using traditional and embedded denoising against a baseline approach (without denoising) is yet to be performed. To analyze the impact of denoising on the CNN performance, in this paper, firstly, we filter the noise from the images using traditional means of denoising method before their use in the CNN model. Secondly, we embed a denoising layer in the CNN model. To validate the performance of image denoising, we performed extensive experiments for both traffic sign and object recognition datasets. To decide whether denoising will be adopted and to decide on the type of filter to be used, we also present an approach exploiting the peak-signal-to-noise-ratio (PSNRs) distribution of images. Both CNN accuracy and PSNRs distribution are used to evaluate the effectiveness of the denoising approaches. As expected, the results vary with the type of filter, impact, and dataset used in both traditional and embedded denoising approaches. However, traditional denoising shows better accuracy, while embedded denoising shows lower computational time for most of the cases. Overall, this comparative study gives insights into whether denoising will be adopted in various CNN-based image analyses, including autonomous driving, animal detection, and facial recognition.

show abstract

An Image Enhancement Method for Autonomous Vehicles Driving in Poor Visibility Circumstances

Cited by 2 publications

References 27 publications

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

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

Impact of Traditional and Embedded Image Denoising on CNN-Based Deep Learning

Contact Info

Product

Resources

About