Denoising module for wood texture images
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The need for an effective automatic wood species identification system is becoming critical in the timber industry with the intention to sustain and improve productivity and quality of the timber products in furniture industry and housing industry. The first stage in an automatic wood recognition system is the image acquisition process where wood images are captured and stored in the database. Good quality wood images must be obtained during the acquisition process in order to guarantee effective results. One of the main issues in identifying wood species effectively is the blurred images of wood texture captured during the image acquisition process. To cater the above-mentioned problem, wood image denoising process is crucial for the timber industry. An image denoising module is proposed to improve the image representation of the wood texture by using the expectation–maximization (EM) adaption algorithm. Then, image quality assessment techniques are applied to evaluate the quality of the denoised wood images. Finally, the performance of the proposed denoising technique is compared to several denoising techniques at various noise levels. In this research, 52 wood species are used where the size of each wood image is 768 × 576 pixels with 256 gray levels at 300 dpi resolution. Experimental results tabulate the mean and standard deviation of the image quality assessment values for each technique at various noise levels. It can be seen that the proposed method EM adaption filter gives the best peak signal-to-noise ratio performance compared to other techniques. In conclusion, the proposed EM adaptation method gives the best performance in denoising the wood texture images at various noise levels compared to other techniques, such as homomorphic filtering, direct inverse filter, Wiener filter, constrained least squares, Lucy–Richardson algorithm, and EM filter.
Funding was provided by RU Grant—Faculty Programme by Faculty of Engineering, University of Malaya with project number RF001A-2018.
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