Wavelet based edge feature enhancement for convolutional neural networks

Silva, Dedimuni D. De; Fernando, Subha; Piyatilake, I. T. S.; Karunarathne, Ajith

doi:10.1117/12.2522849

Cited by 13 publications

(13 citation statements)

References 27 publications

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“…Moreover, we have compared our findings with several previous work done in the wavelets and neural networks domain. [48] and wavelet based preprocessing for CNN [10] have also compared. Both previous work process inputs in wavelet domain using fixed wavelets without any wavelet learning.…”

Section: Resultsmentioning

confidence: 99%

“…In terms of deep learning, wavelet-based methods have been used in many computer vision applications through Convolutional Neural Networks (CNN) due its ability of diverse information extraction. Texture classification using wavelet CNN [11], multi-scale face super-resolution [16], image super-resolution [13] and edge feature enhancing [10] are notable applications. A multi-level wavelet CNN model for image restoration has been introduced by Liu et al [21].…”

Section: Related Workmentioning

confidence: 99%

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Multi-path learnable wavelet neural network for image classification

Silva

Vithanage

Fernando³

et al. 2020

Twelfth International Conference on Machine Vision (ICMV 2019)

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Despite the remarkable success of deep learning in pattern recognition, deep network models face the problem of training a large number of parameters. In this paper, we propose and evaluate a novel multi-path wavelet neural network architecture for image classification with far less number of trainable parameters. The model architecture consists of a multi-path layout with several levels of wavelet decompositions performed in parallel followed by fully connected layers. These decomposition operations comprise wavelet neurons with learnable parameters, which are updated during the training phase using the back-propagation algorithm. We evaluate the performance of the introduced network using common image datasets without data augmentation except for SVHN and compare the results with influential deep learning models. Our findings support the possibility of reducing the number of parameters significantly in deep neural networks without compromising its accuracy.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Multi-path learnable wavelet neural network for image classification

Silva

Vithanage

Fernando³

et al. 2020

Twelfth International Conference on Machine Vision (ICMV 2019)

View full text Add to dashboard Cite

show abstract

“…Cai et al 6 used the image processing of intelligent buildings as the basic theoretical platform, and based on the pattern recognition technology, studied image processing, image extraction, and image recognition in image processing of building intelligent environment, and proposed reasonable solutions. De Silva et al 7 proposed and evaluated two wavelet-based edge feature enhancement methods to pre-process the input image by CNN, developed a new pre-processing layer, and attached it to the network architecture. The results show that the proposed method is superior to the baseline and previously published work.…”

Section: Related Workmentioning

confidence: 99%

A novel image edge smoothing method based on convolutional neural network

2020

International Journal of Advanced Robotic Systems

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In the field of visual perception, the edges of images tend to be rich in effective visual stimuli, which contribute to the neural network’s understanding of various scenes. Image smoothing is an image processing method used to highlight the wide area, low-frequency components, main part of the image or to suppress image noise and high-frequency interference components, which could make the image’s brightness smooth and gradual, reduce the abrupt gradient, and improve the image quality. At present, there are still problems such as easy blurring of the edges of the image, poor overall smoothing effect, obvious step effect, and lack of robustness to noise on image smoothing. Based on the convolutional neural network, this article proposes a method for edge detection and deep learning for image smoothing. The results show that the research method proposed in this article solves the problem of edge detection and information capture better, significantly improves the edge effect, and protects the effectiveness of edge information. At the same time, it reduces the signal-to-noise ratio of the smoothed image and greatly improves the effect of image smoothing.

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“…Various methods utilizing wavelet-based features in neural networks have also been explored to capitalize on multiscale features of wavelets in the computer vision domain [37][38][39]. Multiscale convolutional neural networks have also been used for classification [40,41]. Fully convolutional networks have shown improved performance for classification [42][43][44].…”

Section: Related Workmentioning

confidence: 99%

Identifying Informal Settlements Using Contourlet Assisted Deep Learning

Ansari

Malhotra

Buddhiraju

2020

Sensors

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As the global urban population grows due to the influx of migrants from rural areas, many cities in developing countries face the emergence and proliferation of unplanned and informal settlements. However, even though the rise of unplanned development influences planning and management of residential land-use, reliable and detailed information about these areas is often scarce. While formal settlements in urban areas are easily mapped due to their distinct features, this does not hold true for informal settlements because of their microstructure, instability, and variability of shape and texture. Therefore, detecting and mapping these areas remains a challenging task. This research will contribute to the development of tools to identify such informal built-up areas by using an integrated approach of multiscale deep learning. The authors propose a composite architecture for semantic segmentation using the U-net architecture aided by information obtained from a multiscale contourlet transform. This work also analyzes the effects of wavelet and contourlet decompositions in the U-net architecture. The performance was evaluated in terms of precision, recall, F-score, mean intersection over union, and overall accuracy. It was found that the proposed method has better class-discriminating power as compared to existing methods and has an overall classification accuracy of 94.9–95.7%.

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Wavelet based edge feature enhancement for convolutional neural networks

Cited by 13 publications

References 27 publications

Multi-path learnable wavelet neural network for image classification

Multi-path learnable wavelet neural network for image classification

A novel image edge smoothing method based on convolutional neural network

Identifying Informal Settlements Using Contourlet Assisted Deep Learning

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