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Image enhancement effect on the performance of convolutional neural networks
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Context. Image enhancement algorithms can be used to enhance the visual effects of images in the field of human vision. So can image enhancement algorithms be used in the field of computer vision? The convolutional neural network, as the most powerful image classifier at present, has excellent performance in the field of image recognition. This paper explores whether image enhancement algorithms can be used to improve the performance of convolutional neural networks.

Objectives. The purpose of this paper is to explore the effect of image enhancement algorithms on the performance of CNN models in deep learning and transfer learning, respectively. The article selected five different image enhancement algorithms, they are the contrast limited adaptive histogram equalization (CLAHE), the successive means of the quantization transform (SMQT), the adaptive gamma correction, the wavelet transform, and the Laplace operator.

Methods. In this paper, experiments are used as research methods. Three groups of experiments are designed; they respectively explore whether the enhancement of grayscale images can improve the performance of CNN in deep learning, whether the enhancement of color images can improve the performance of CNN in deep learning and whether the enhancement of RGB images can improve the performance of CNN in transfer learning?Results. In the experiment, in deep learning, when training a complete CNN model, using the Laplace operator to enhance the gray image can improve the recall rate of CNN. However, the remaining image enhancement algorithms cannot improve the performance of CNN in both grayscale image datasets and color image datasets. In addition, in transfer learning, when fine-tuning the pre-trained CNN model, using contrast limited adaptive histogram equalization (CLAHE), successive means quantization transform (SMQT), Wavelet transform, and Laplace operator will reduce the performance of CNN.

Conclusions. Experiments show that in deep learning, using image enhancement algorithms may improve CNN performance when training complete CNN models, but not all image enhancement algorithms can improve CNN performance; in transfer learning, when fine-tuning the pre- trained CNN model, image enhancement algorithms may reduce the performance of CNN.

Place, publisher, year, edition, pages
2019. , p. 40
Keywords [en]
Image Enhancement, Convolutional Neural Networks, Deep Learning, Transfer Learning
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:bth-18523OAI: oai:DiVA.org:bth-18523DiVA, id: diva2:1341096
Subject / course
DV2572 MasterĀ“s Thesis in Computer Science
Educational program
DVACS Master of Science Programme in Computer Science
Supervisors
Examiners
Available from: 2019-08-12 Created: 2019-08-07 Last updated: 2019-08-12Bibliographically approved

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CiteExportLink to record
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Citation style
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