476 results about "Bilateral filter" patented technology

Systems and methods for extended color processing on Pelican array cameras in accordance with embodiments of the invention are disclosed. In one embodiment, a method of generating a high resolution image includes obtaining input images, where a first set of images includes information in a first band of visible wavelengths and a second set of images includes information in a second band of visible wavelengths and non-visible wavelengths, determining an initial estimate by combining the first set of images into a first fused image, combining the second set of images into a second fused image, spatially registering the fused images, denoising the fused images using bilateral filters, normalizing the second fused image in the photometric reference space of the first fused image, combining the fused images, determining a high resolution image that when mapped through a forward imaging transformation matches the input images within at least one predetermined criterion.

A method of performing bilateral filtering and using the method for displaying high-dynamic-range images is presented. The method reduces the contrast of the image while preserving detail of the image. The presently disclosed method incorporates a two-scale decomposition of the image into a base layer encoding large-scale variations, and a detail layer. The base layer has its contrast reduced, thereby preserving detail. The base layer is obtained using an edge-preserving bilateral filter. The bilateral filter is a non-linear filter, where the weight of each pixel is computed using a Gaussian in the spatial domain multiplied by an influence function in the intensity domain that decreases the weight of pixels with large intensity differences. The bilateral filtering is accelerated by using a piecewise-linear approximation in the intensity domain and appropriate subsampling.

The invention discloses a binocular stereoscopic vision matching method combining depth characteristics. The binocular stereoscopic vision matching method comprises: obtaining a depth characteristic pattern from left and right images through a convolutional neural network; calculating a truncation similarity measurement degree of pixel depth characteristics by taking the depth characteristics as the standard, and constructing a truncation matching cost function combining color, gradients and depth characteristics to obtain a matched cost volume; processing the matched cost volume by adopting a fixed window, a variable window and a self-adaptive weight polymerization or guide filtering method to obtain a cost volume polymerized by a matching cost; selecting an optimal parallax error of the cost volume by adopting WTA (Wireless Telephony Application) to obtain an initial parallax error pattern; then finding a shielding region by adopting a double-peak test, left-right consistency detection, sequence consistency detection or shielding constraint algorithm, and giving a shielding point to a parallax error value of a same-row point closest to the shielding point to obtain a parallax error pattern; and filtering the parallax error pattern by adopting a mean value or bilateral filter to obtain a final parallax error pattern. By adopting the binocular stereoscopic vision matching method combining the depth characteristics, the incorrect matching rate of three-dimensional matching can be effectively reduced, the images are smooth and image edges including edges of small objects are effectively kept.

The invention discloses a color digital image enhancing and denoising method under random illumination. The method is characterized by comprising the following steps of: 1, image denoising processing, namely converting an original image from an RGB space to a YCbCr space, removing a Gaussian noise by using a Gaussian filter and removing a salt and pepper noise by performing median filter; 2, image brightness / contract stretch processing, converting the image from the RGB space to an HSI space, decomposing the image by using a two-sided filter, processing by an improved Retinex model algorithm, and obtaining a new image saturation by performing saturation compensation; and 3, performing fusion display on the image acquired after denoising the YCbCr space and the image acquired by HSI space processing. By the method, the color constancy of the image can be kept, the dynamic range of the image can be improved well, and simultaneously noises of the image can be inhibited and removed with less texture and detail information loss.

Methods and apparatus for diffuse indirect illumination computation using progressive interleaved irradiance sampling. Embodiments may implement a method that amortizes the cost of computing the irradiance integral for diffuse indirect illumination both temporally and spatially in screen space. For each pixel, only one secondary ray is fired. By carefully arranging different secondary ray directions for different pixels according to a sampling sequence, embodiments may filter the noisy estimate so that each pixel receives a relatively uniform coverage of the integrated hemisphere. Some embodiments may use a bilateral filter so that the geometric discontinuities are respected. The sequence may continue to a higher-level of stratification in each frame. This ensures that the rendering is converging to a noise-free result.

A luminance intensity of pixels of an input digital image is corrected for generating a corrected digital image. A luminance of each pixel is calculated as a function of the luminance of a corresponding pixel in an original image according to a parametric function. A mask of the input digital image to be corrected is generated according to a bilateral filtering technique. For each pixel of the input digital image, a respective value of at least one parameter of the parametric function is established based upon the luminance of a corresponding pixel of the mask.

The invention provides a superpixel method for medical image segmentation. The method comprises the steps of: processing a medical image into a superpixel; for the medical images obtained after superpixel segmentation, using bilateral filtering to preserve the edge and filter the noise to reduce the error rate of the network model; configuring a network framework, and constructing a convolution network for the medical images obtained after superpixel segmentation by iterative training parameters. Based on the linear iterative clustering segmentation method, this method applies the thought of the U-Net network to the post-optimization of super-pixels, which makes up the defect of inaccurate segmentation of inner edge of super-pixel, increases the standard layer to improve the weight sensitivity of each network layer, improves the convergence performance of the network, and makes the segmentation result closer to the actual value. Because the anatomical structure and pathological tissueof medical images are very clear, the medical images segmented by SLIC algorithm can obtain more comprehensive super-pixel, and the edge accuracy of super-pixel can be further improved by convolutionnetwork.

A method for processing an image using a bilateral filter. The bilateral filter is reformulated at each pixel location in the image into a sum of the original signal value of a central pixel at said pixel location and a bilateral correction term which is a function of local signal differences between the central pixel and its neighbors. The bilateral correction term is calculated using a computationally efficient approximation.

The invention discloses a color image enhancement method based on bright channel filtering. The color image enhancement method based on bright channel filtering mainly solves the problems that in the prior art, halo artifacts exist on the edge of an object in an enhancement result image, and time is consumed in the process of treatment. The color image enhancement method based on bright channel filtering comprises the steps of (1) maximizing RGB components of each pixel of a color image to be enhanced to obtain a bright channel image I (x, y), (2) carrying out grayscale closed operation on the bright channel image I (x, y), defining the result of the grayscale closed operation as an I' (x, y), (3) filtering the result of the grayscale closed operation of the bright channel image with cooperation of the bright channel image I (x, y) through rapid connection of a two-sided filter to obtain an illumination component L (x, y), and (4) substituting the illumination component L (x, y) into an illumination-and-reflection imaging model, solving reflection components of RGB color channels of an input image, extending the reflection components of the color channels to the interval [0, 255] to obtain a color image, and using the color image as a final enhancement result. According to the color image enhancement method based on bright channel filtering, only a single input image is needed, a darker image can be enhanced, shadows of the image with uneven illumination can be eliminated, and the color image enhancement method based on bright channel filtering can be used for preprocessing of detection, tracking and identification of targets.

A method of producing a compressed video data stream (12) by compressing a stream of relatively high dynamic range video frames (2). A bilateral filter (3) extracts a base frame (4) with luminance information. The base frame (4) and the original frame (2) are used to provide a detail frame (5) with chroma information. A tone mapping operation (6) is selected (7, 11) and applied to the base frame to generate a relatively low dynamic range base frame (8), which is then compressed (9). The detail frame (5) is compressed separately. Final frame data (12) is then created, consisting of the compressed relatively low dynamic range base frame, the compressed detail frame, and stored information in respect of the tone mapping operation that had been applied to the base frame.

A method and apparatus for generating a dense depth map. In one embodiment, the method includes applying a joint bilateral filter to a first depth map to generate a second depth map, where at least one filter weight of the joint bilateral filter is adapted based upon content of an image represented by the first depth map, and the second depth map has a higher resolution than the first depth map.

The invention relates to a facial beautification method and electronic equipment in a real-time video. The facial beautification method comprises the steps of obtaining video frames through a camera; determining skin color areas and non-skin color areas through skin color detection, respectively converting an original image into HSV (Hue-Saturation-Value) and RGB (Red-Green-Blue) spaces, determining areas within a range threshold of limited skin color in the HSV and RGB color spaces as the skin color areas and other areas as the non-skin-color areas, and separating the skin areas from the image; integrally performing pyramid bilateral filtering on the original image, wherein noisy points are considered if a certain threshold is exceeded, and performing medium filtering on the noisy points; directly replacing the skin areas in the original image with images after filtering, and fusing the original image and the images after filtering by the non-skin color areas. By adopting the facial beautification method, the skin color of figures in the video is obviously beautified, the facial beautification efficiency in the real-time video is improved, and the user experience effect is increased.

The invention belongs to the technical field of image and video processing, and particularly discloses a high dynamic range image compression method based on a mixed tone mapping algorithm. According to the high dynamic range image compression method, the problem about image distorsion when a high dynamic image displays on general display equipment is mainly solved. The high dynamic range image compression method comprises the following steps: firstly, using global linear compression based on image types to save the integral effect of the image; secondly, further enhancing the naturalness of the image by using an adaptive avoiding and value burning technology; finally, improving the visibility of image detailed information by using a local tone mapping algorithm based on a dual-side filter. According to the high dynamic range image compression method disclosed by the invention, a natural distinct low dynamic image result can be obtained; the high dynamic range image compression method can be widely applied to relevant fields of image and video processing.

The invention provides an image denoising method based on a self-adaptive wavelet threshold and a two-sided filter and aims to improve the effect of a wavelet threshold denoising algorithm and better protect the edge and the detailed information of an image. The algorithm comprises the following steps of decomposing the image by adopting a discrete wavelet to obtain a plurality of sub-bands and the wavelet coefficients of the sub-bands; selecting a threshold which is self-adaptively changed along with the changes of wavelet-decomposing scales and the sub-bands, and carrying out quantitative threshold processing by adopting a soft threshold function; carrying out inverse wavelet transformation to obtain a reconstructed image; filtering the reconstructed image by adopting the two-sided filter so as to obtain the clear image. According to the image denoising method, wavelet threshold denoising is carried out by utilizing the threshold which is self-adapted to the wavelet-decomposing scales and the sub-bands, and filtering is carried out by combining the two-sided filter, so that through the designed denoising algorithm, not only can white gaussian noise be effectively removed, but also the edge and the detailed information of the image can be well reserved.

The invention provides a Retinex image enhancement method based on a Laplacian pyramid. The method comprises the following three processes: firstly, carrying out multi-scale convolution on an originalimage, carrying out convolution by adopting three 3 * 3 Gaussian convolution kernels, and obtaining R, G and B channel feature maps with different scales; secondly, estimating an incident component by adopting a Retinex algorithm and enhancing colors; enhancing details of reflective components; and finally, carrying out linear weighted fusion on the color enhanced image and the detail enhanced image to obtain an enhanced image. According to the method, more details of the image are acquired by adopting multi-scale convolution, image enhancement is carried out by utilizing an MSRCR algorithm,and Gamma correction is carried out on an incident component to realize color enhancement. Bilateral filtering denoising is carried out on the reflection component to reduce noise interference, and image detail information is extracted through a Laplacian pyramid; and linear weighted fusion is carried out on the color enhanced image and the detail enhanced image to realize image enhancement.

The invention produces a higher quality image from a rendering system based on a relationship between the output of a rendering system and the parameters used to compute them. Specifically, noise is removed in rendering by estimating the functional dependency between sample features and the random inputs to the system. Mutual information is applied to a local neighborhood of samples in each part of the image. This dependency is then used to reduce the importance of certain scene features in a cross-bilateral filter, which preserves scene detail. The results produced by the invention are computed in a few minutes thereby making it reasonably robust for use in production environments.

The invention belongs to the technical field of image processing, and particularly discloses a multi-exposure image integration method based on bilateral filtering. The method comprises the steps that (1) three static pictures of the same scene of different exposures are inputted; (2) the originally inputted pictures are divided into two parts of a detail layer and a basic layer based on a bilateral filter and then the detail layer and the basic layer are processed respectively; (3) weight of the basic layer is estimated by applying a Gaussian curve and weight is updated by applying picture saturation information; (4) an intermediate integration picture is preliminarily integrated by the updated weight; (5) the maximum value of each detail layer is obtained to act as a detail picture as for the detail layer; and (6) a final integration picture is obtained by synthesizing the obtained intermediate integration picture and the detail picture. Calculation complexity is low, and the details of the picture can be fully protected in the integration picture and the picture is enabled to be more natural in the aspect of color. The multi-exposure image integration method based on bilateral filtering can be widely applied to the field of image enhancement or cameras.

The invention provides a single image rainfall removing method based on image partitioning of generating antagonistic network. By dividing images into image blocks with the same size and non-overlapping each other, each image block is used as a condition to generate input of antagonistic network, and the input dimension is reduced. A generate antagonism network is trained to realize the nonlinearmapping from rainless image block to rainless image block, which overcomes the problem that many details are neglected and can remove the rainline at every scale as much as possible. In order to maintain the consistency of the structure and color between the rainless image blocks, a new error function is constructed by using bilateral filter and non-mean local denoising algorithm, which is added to the total error function of the conditional generation countermeasure network. The invention does not need any prior knowledge, nor does it need to preprocess and post-process the image, thus ensuring the integrity of the whole structure. The results on the test set show that the invention is improved by 4-7dB compared with the classical algorithm.

The present invention relates to a semantic object segmentation method suitable for low field depth image, which includes: firstly introducing gradient histogram to figure out the distribution of image in the energy space, obtaining an energy focusing significance map in combination with the character of the low field depth map; using the two-sided filter and morphology instrument to rehandle the energy focusing significance map; and then setting self-adapting threshold value and processing to obtain an initial object mask map, combining with the edge information obtained by canny operator to obtain the corrected object mask, in order to raise the segmentation accuracy of the interesting object; finally using the Bayesian eclosion algorithm to obtain the ideal semantic object segmentation result, in order to delicately process the complicated image boundary, such as hairs. Accurate segmentation to the interesting object in the field depth scope in the image video sequence.

Projection data acquired with an x-ray CT system is filtered using a bilateral filter to reduce image noise and enable the acquisition at lower x-ray dose without the loss of image diagnostic quality. The bilateral filtering is performed before image reconstruction by producing a noise equivalent data set from the acquired projection data and then converting the bilateral filtered values back to a projection data set suitable for image reconstruction.

The invention produces a higher quality image from a rendering system based on a relationship between the output of a rendering system and the parameters used to compute them. We propose a method that robustly combines color and feature buffers to denoise Monte Carlo renderings. On one hand, feature buffers, such as per pixel normals, textures, or depth, are effective in determining denoising filters because features are highly correlated with rendered images. Filters based solely on features, however, are prone to blurring image details that are not well represented by the features. On the other hand, color buffers represent all details, but they may be less effective to determine filters because they are contaminated by the noise that is supposed to be removed. We propose to obtain filters using a combination of color and feature buffers in an NL-means and cross-bilateral filtering framework. We determine a robust weighting of colors and features using a SURE-based error estimate. We show significant improvements in subjective and quantitative errors compared to the previous state-of-the-art. We also demonstrate adaptive sampling and space-time filtering for animations.

The invention discloses a finger multimodal preprocessessing method based on maximum circumscribing matrix region-of-interest extract and bilateral filtering. The method comprises the following steps: firstly, gathering finger vein and finger phalangeal print images; secondly, performing finger multimodal preprocessessing on the gathered images, based on maximum circumscribing matrix region-of-interest extract and bilateral filtering, and removing background areas in the finger images through binaryzation, obtaining a final finger vein ROI area by solving the maximum circumscribing matrix among key areas, and then cutting an finger phalangeal print ROI area in the finger phalangeal print image according to the finger vein ROI area; finally normalizing the images, and performing filtering on the normalized images through a bilateral filter; thirdly, performing image layer fusion on the processed images, based on wavelet transform and Gabor filtering. The method is rich in information and high in security.

The invention discloses an image restoration method based on a double-edge wave filter and margin deconvolution, comprising the following steps: (1) obtaining a restoration image with rings by adopting a standard RL algorithm to carry out higher iteration according to an image to be restored and a known fuzzy core; (2) adopting a double-edge wave filter to carry out filtering operation of the restoration image with rings to obtain the restoration image without rings; (3) carrying out the deconvolution on the restoration image without rings by the known fuzzy core to obtain a re-fuzzy image; (4) obtaining a restoration image detail by adopting the margin deconvolution by the re-fuzzy image obtained by the step (3) and the image to be restored, and adding the restoration image detail with the restoration image after eliminating ring to obtain a new restoration image containing ring; and (5) repeating the steps (2)-(4) for many times to obtain a final restoration image. The image restoration method can effectively inhibit middle and high frequency rings in the restoration image of the iterative algorithm, remain rich image details and obtain high-quality restoration images.

The invention discloses a high dynamic range image tone mapping method based on a retina adaptive model, which belongs to the field of digital image processing. The method comprises the following steps: (1) inputting a high dynamic range image; (2) acquiring the logarithm domain luminance L(p) of each pixel p in the image; (3) carrying out two-sided filtering on the logarithm domain luminance L(p) of each pixel p by using a two-sided filter, thereby obtaining the adaptive factor Aopl(p); (4) obtaining the luminance Lram(p) after carrying out nonlinear mapping on the logarithm domain luminance L(p) of each pixel p; (5) obtaining a red channel value I'(pR), a green channel value I'(pG) and a blue channel value I'(pB) after respectively carrying out nonlinear mapping on each pixel p in a red channel R, a green channel G and a blue channel B; and (6) constructing a new image file by using I'(pR), I'(pG) and I'(pB), thereby realizing the tone mapping of the high dynamic range image. The method simultaneously has the advantage of high speed in the global approach and the advantage of high capability of reserving image details in the partial approach.

An image matching apparatus includes a bilateral filter that filters a left image and a right image to output a second left image and a second right image; a census cost calculation unit performing census transform on a window based on a first pixel of the second left image and a window based on a second pixel of the second right image to calculate a census cost corresponding to a pair of pixels of the first and second pixels; a support weight calculation unit obtaining support weights of the left and right images or the second left and second right images; a cost aggregation unit obtaining energy values of nodes corresponding to the pair of pixels of the first and second pixels using the census cost and the support weights; and a dynamic programming unit performing image matching using dynamic programming by the energy values of each node obtained.

The invention discloses a bilateral filtering de-noising method of point cloud data based on KDTree when a ground laser scanning technology is used to carry out measured object surface reconstruction. In a bilateral filtering algorithm, a scattered point cloud data point field is established through the KDTree so as to obtain a field point set of the data point. An inverse iteration method is used to calculate the field point so as to obtain a normal vector ni. A filtering function parameter and a filtering Gauss parameter are calculated so as to obtain a bilateral filtering power factor lambda. Finally, a new data point after the filtering can be obtained, wherein the new data point p'i=pi+ lambdani. The bilateral filtering method of the invention is used to filter the noise of the point cloud data. The method is simple and effective. An operation speed is fast. A characteristic can be maintained and simultaneously the noise can be removed. The method is suitable for processing the noise in a small scope.

The invention discloses a depth image preprocessing method. A maximally tolerant distortion distribution image of a depth image is obtained according to the influence of depth distortion on the rendering of a virtual viewpoint image in combination with the visual characteristics of a human eye, the depth image is divided into a belief content area and an unbelief content area according to the maximally tolerant distortion distribution image, and two groups of bilateral filters with different filtering intensities are designed to filter depth values of each pixel in the belief content area andthe unbelief content area. The method has the advantage that: the filtering intensity is selected according to the maximally tolerant distortion distribution image of the depth image to greatly improve the compression efficiency of the depth image on the basis of ensuring the rendering performance of the virtual viewpoint image.

The invention discloses a shearlet transform and fast bilateral filter image denoising method, which comprises the steps of: 1) acquiring an envelope signal of a noise image by using a noise imaging system, and establishing a medical ultrasonic image model; 2) carrying out multiscale and multidirectional decomposition on the medical ultrasonic image model after logarithmic transformation obtained in the step 1) by utilizing a pyramid filter bank; 3) performing threshold method contraction processing on a two-dimensional discrete shearlet transform coefficient of a high-frequency part in each subband image obtained in the step 2); 4) using a fast bilateral filter for filtering shearlet coefficients of low-frequency parts in the step 2); 5) and conducting shearlet inverse transform processing on all the coefficients processed in the step 3) and the step 4), so as to obtain a denoised medical ultrasound images. The introduction of the fast bilateral filter in the shearlet transform and fast bilateral filter image denoising method can effectively improve the denoising performance, and greatly increase the processing efficiency.