Bilateral filter

About: Bilateral filter is a research topic. Over the lifetime, 3500 publications have been published within this topic receiving 75582 citations.

An image denoising method based on multiscale wavelet thresholding and bilateral filtering

Abstract: A novel image denoising method is proposed based on multiscale wavelet thresholding (WT) and bilateral filtering (BF). First, the image is decomposed into multiscale subbands by wavelet transform. Then, from the top scale to the bottom scale, we apply BF to the approximation subbands and WT to the detail subbands. The filtered subbands are reconstructed back to approximation subbands of the lower scale. Finally, subbands are reconstructed in all the scales, and in this way the denoised image is formed. Different from conventional methods such as WT and BF, it can smooth the low-frequency noise efficiently. Experiment results on the image Lena and Rice show that the peak signal-to-noise ratio (PSNR) is improved by at least 3 dB and 0.7 dB compared with using the WT and BF, respectively. In addition, the computational time of the proposed method is almost comparable with that of WT but much less than that of BF.

Image reduction of binary images using filtering processing

Abstract: An image reduction system which reduces binary level images is disclosed. In the system, a reduction pixel value is determined by using, as reference pixels for a filter operation: a plurality of surrounding pixels including an object pixel to be reduced; and reduced pixels, from among pixels which have already been reduced, said reduced pixels being at least; a reduced pixel located before the pixel to be determined: a reduced pixel, located on the preceding line, which lies directly above the pixel to be determined; and a reduced pixel before said reduced pixel located on the preceding line. When a pixel referenced during the filter operation is included in a specific pattern, an exception process for correcting binary level results is provided.

System and method of scaling images using adaptive nearest neighbor

Abstract: A method for interpolating a target pixel from a plurality of source pixels in a high contrast image. The method comprises the following steps. A window of the plurality of source pixels is examined and compared with a plurality of predefined conditions for determining if a structure of significance is present within the window. A filter configuration is selected from a plurality of filter configurations in accordance with results of the comparison. The selected filter is applied to the source pixels for interpolating the target pixel. If the structure of significance is detected in the window, the selected filter best preserves the structure.

Retinal Image Denoising via Bilateral Filter with a Spatial Kernel of Optimally Oriented Line Spread Function.

Abstract: Filtering belongs to the most fundamental operations of retinal image processing and for which the value of the filtered image at a given location is a function of the values in a local window centered at this location. However, preserving thin retinal vessels during the filtering process is challenging due to vessels’ small area and weak contrast compared to background, caused by the limited resolution of imaging and less blood flow in the vessel. In this paper, we present a novel retinal image denoising approach which is able to preserve the details of retinal vessels while effectively eliminating image noise. Specifically, our approach is carried out by determining an optimal spatial kernel for the bilateral filter, which is represented by a line spread function with an orientation and scale adjusted adaptively to the local vessel structure. Moreover, this approach can also be served as a preprocessing tool for improving the accuracy of the vessel detection technique. Experimental results show the superiority of our approach over state-of-the-art image denoising techniques such as the bilateral filter.

Spatial and Color Adaptive Gamut Mapping: A Mathematical Framework and Two New Algorithms.

Abstract: A general framework for adaptive gamut mapping is presented in which a wide range of published spatial gamut mapping algorithms fit. Two new spatial and color adaptive gamut mapping algorithms are then introduced. Based on spatial color bilateral filtering, they take into account the color properties of the neighborhood of each pixel. Their goal is to preserve both the color values of the pixels and their relations between neighbors. Results of psychophysical experiments confirm the good performance of the proposed algorithms.