Image smoothing has been used for image segmentation, image reconstruction, object classification, and 3D content generation. Several smoothing approaches have been used at the pre-processing step to retain the critical edge, while removing noise and small details. However, they have limited performance, especially in removing small details and smoothing discrete regions. Therefore, to provide fast and accurate smoothing, we propose an effective scheme that uses a weighted combination of the gradient, Laplacian, and diagonal derivatives of a smoothed image. In addition, to reduce computational complexity, we designed and implemented a parallel processing structure for the proposed scheme on a graphics processing unit (GPU). For an objective evaluation of the smoothing performance, the images were linearly quantized into several layers to generate experimental images, and the quantized images were smoothed using several methods for reconstructing the smoothly changed shape and intensity of the original image. Experimental results showed that the proposed scheme has higher objective scores and better successful smoothing performance than similar schemes, while preserving and removing critical and trivial details, respectively. For computational complexity, the proposed smoothing scheme running on a GPU provided 18 and 16 times lower complexity than the proposed smoothing scheme running on a CPU and the L0-based smoothing scheme, respectively. In addition, a simple noise reduction test was conducted to show the characteristics of the proposed approach; it reported that the presented algorithm outperforms the state-of-the art algorithms by more than 5.4 dB. Therefore, we believe that the proposed scheme can be a useful tool for efficient image smoothing.

The in-line holography has obvious advantages especially in wider spatial bandwidth over the off-axis holography. However, a direct current (DC)-noise and an unwanted twin image should be separated or eliminated for a high quality reconstruction. Therefore, we introduce an effective Laplace operation-based numerical approach for DC-noise reduction in the in-line holography. The DC-noise in a holographic fringe pattern can be efficiently reduced by applying a second derivative operation while maintaining the two real and twin terms. For the performance evaluation, the proposed method is compared with the existing approaches, including the intensity average subtraction and frequency-domain filtering, which are generally used for the numerical holographic reconstruction. Experimental results show that the proposed method is numerically appropriate, and provides the high quality reconstruction for a digital holography. Therefore, we believe that the proposed scheme can be a useful tool for high quality reconstruction in the in-line holography.