Affiliation:
1. Institute of Innovation Science and Sustainability, Federation University Australia, Ballarat 3350, Australia
Abstract
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.
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference55 articles.
1. A systematic review on foggy datasets: Applications and challenges;Juneja;Arch. Comput. Methods Eng.,2022
2. Reviewnet: A fast and resource optimized network for enabling safe autonomous driving in hazy weather conditions;Mehra;IEEE Trans. Intell. Transp. Syst.,2020
3. Perception and sensing for autonomous vehicles under adverse weather conditions: A survey;Zhang;ISPRS J. Photogramm. Remote Sens.,2023
4. Kaur, R., Karmakar, G., and Xia, F. (2023). Image Processing and Intelligent Computing Systems, CRC Press.
5. Deep learning: Survey of environmental and camera impacts on internet of things images;Kaur;Artif. Intell. Rev.,2023
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Image Denoising with CNN-Based Attention;2023 4th International Informatics and Software Engineering Conference (IISEC);2023-12-21