| Summary: | 碩士 === 國立臺灣科技大學 === 資訊工程系 === 107 === In the past, the methods of denoising depth images were usually by using a specific module for the specific type of noise. In recent years, the deep learning technique is employed to denoise general images. We propose to use the deep learning method, which is based on the Convolutional Neural Networks (CNN) to denoise the depth images. We also research that under a single training module how many types of noise can be reduced and how wide the noise level range can be handled. To generate the training sets and testing sets, we use Blender to produce depth images from random 3D scenes. The types of noise we employed in this study are the Additive white Gaussian noise (AWGN), the Salt & Pepper Noise, the Speckle Noise, the Rayleigh Noise, and the Lognormal Noise. When the noise level is set to 15, the AWGN, the Salt & Pepper Noise, and the Speckle Noise denoising can achieve the highest PSNR mean of 41.30 dB, 37.19 dB. and 35.93 dB, respectively. The Riley noise denoising is a special case, the PSNR mean is 15 dB after denoising but the denoised images are visually acceptable. The Lognormal Noise denoising is a failed case.
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