Recently a filtering method based on side windows has been reported. Although this method has strong ability to preserve edge structure, its ability to smooth textures is weaker than using a full window. In this paper, we propose a scale adaptive side window based bilateral filter (SASWBF). An expanding ratio parameter is designed to control the side window varying to the full window. To adapt the size of spatial kernel at each pixel, the scale of filtering window is estimated using a structure measure. A bilateral filter using Fourier basis is adopted to accelerate the filter processing. The computational complexity of the proposed smoothing filter does not depend on the window size and thus is in almost constant time. Our experimental results demonstrates that the proposed filter performs well.
Due to its simplicity, median filter is a very famous and useful tool in the fields such as image processing and computer graphics. Median filter is mainly for eliminating irrelevant details, especially removing salt-and- pepper noises in image. It has the ability to preserve structural edges compared with box filter and Gaussian filter, however, this ability is very limited. When the radius of filter window becomes larger, the edge-preserving ability also becomes very weak. In this paper, we propose a median-like filter that removes small details including salt-and-pepper noises in image while having stronger edge-preserving ability than classical median filter. The filter computes the output at the observed pixel using 8 sub-windows and a full window. Among these windows, 4 of them are built on the 4 quadrants respectively, other 4 of them are on left, right, top, and bottom half planes. All of these sub-windows contain the observed pixel. Moreover, since medians are computed from histograms, we update column histograms and kernel histograms by simple subtraction and addition operations to accelerate the filtering. The computational complexity of the proposed median-like filter is independent of window size and thus is in constant time. A SSIM (Structural SIMilarity) evaluation demonstrates that the proposed median-like filter performs well.
We propose a method to restore the original condition of the cultural properties in the photographed image using extraction and texture synthesis of the degraded parts. It is judged whether or not degradation such as a crack has appeared in each block divided from the image by machine learning. The cracks in the selected block are extracted by binarizing the pixel values and restored with the texture synthesis technique. We confirmed by our computer simulation experiments that the proposed method showed good results.
As a well known filter in the category of order statistic filters, median filter has been widely employed in the fields of image processing, computer graphics and computer vision etc. This paper proposes an approach for implementing the median filter with varying kernel sizes at each different pixels. The proposed algorithm consists of two kinds of histograms. One is column histograms for all columns, the other is a kernel histogram for kernel window. The column histograms are updated according to the scales when moving to the next row, while the kernel histogram is updated using column histograms when moving to the next pixel. Since the kernel sizes are varying at each pixel, the updating of column and kernel histograms uses operations of addition and subtraction depending on the scales. Compared with the brute-force implementation, the experiments show that the proposed algorithm is very fast and effective.
Filtering image by eliminating irrelevant details as could as possible while preserving structure edge and corner becomes very important in the fields such as image processing and computer vision etc. In this paper, we present an Anisotropic Rolling Filter (ARF) for smoothing image while preserving important structure edge and corner. The proposed filter implements an extended cross bilateral filter, in which the range weights are updated in an iterative manner, while the spatial Gaussian weights are computed in anisotropic directions instead of isotropic directions. The anisotropic directions are computed based on structure tensors which are calculated at each pixel to determine structure orientations. Compared with the original rolling guidance filter, it is found that the proposed anisotropic rolling filter has stronger smoothing and structure preserving ability.
An exposure method is proposed to test the focusing properties of subwavelength photon sieves. To solve the problems caused by the subwavelength photon sieves (such as short focal length and small focal spot size), a grating moiré fringe phase detection technique and a microcontact sensor with lead zirconium titanate (PZT) stepping hybrid technique are used in the experimental setup. The focusing properties of the subwavelength photon sieves are tested by this setup. The results show that the focal length and the focal spot size are close to the designed value. Finally, the intensity distribution of the focal spot is proposed. This research result will be beneficial for understanding the focusing properties of subwavelength photon sieves, will help us to improve the imaging quality, and will provide a good experimental basis for practical applications in the nanolithography field.