396 results about "Gaussian blur" patented technology

The invention provides a blurred photos generation method, blurred photos generation device and a mobile terminal. The blurred photo generation method comprises steps of detecting a contour edge of a shooting subject in a foreground area in a current preview image, obtaining a blurring coefficient for blurring processing on the background area beyond the couture edge in the current preview image, and performing gauss blurring processing on the background area according to the blurring coefficient to generate a blurred photo. The blurred photo generation method can prevent the shooting subject from being blurred or can prevent the background from not being completely blurred and improves blurring accuracy of the photo.

Herein is presented a catadioptric projector by combining a commodity digital projector with additional optical units. By using specially shaped reflectors and/or refractors, a catadioptric projector can offer an unprecedented level of flexibility in aspect ratio, size, and field of view. Also presented, are methods to reduce projection artifacts in catadioptric projectors, such as distortions, scattering, and defocusing. By analysis of projection defocus of reflector and thin refractor based catadioptric projectors, it is shown that defocus blur can be interpreted as spatially-varying Gaussian blurs on an input image. Kernels are measured directly from a light transport matrix, T, and de-convolution is applied to optimize an input image. Practical uses of catadioptric projectors in panoramic and omni-directional projections are also demonstrated.

The invention relates to a multipoint touch method based on binocular stereoscopic vision, which relates to the technical field of computer vision. The method comprises the following steps: firstly, the azimuth and the projection parameters of a camera relative to a three-dimensional scene are determined through camera calibration, and a binocular camera is utilized to acquire an image pair; secondly, two pictures on the same frame which are acquired on the first step are taken as input, and a scene depth map is calculated by the stereoscopic vision algorithm; thirdly, closest patches are obtained through Gaussian blur and determined thresholds, and central coordinates of the patches are calculated; and fourthly, a plurality of coordinates obtained in the third step are taken as input of a multipoint touch program, and interactive results are returned to a screen. The multipoint touch method based on binocular stereoscopic vision can realize completing the multipoint touch operation in space by a user without touching the screen, and coordinate information of input points acquired is three-dimensional, so that the multipoint touch method can realize more complex interaction such as push and pull of a virtual object and so on.

The present invention proposes a bokeh photograph generation method and device, and a mobile terminal. The bokeh photograph generation method comprises the following steps: determining first depth of field information about a foreground region and second depth of field information about a background region in a current preview image according to preview image data respectively acquired by two rear-facing cameras in a dual-camera device; acquiring a base value of bokeh degree according to the first depth of field information and the second depth of field information; and performing Gaussian blur processing on the background region according to the base value of bokeh degree to generate a bokeh photograph. The bokeh photograph generation method proposed by the present invention can simplify the process of generating a background bokeh photograph when photographing, thereby improving the shooting effiency.

The invention discloses a rapid license-plate positioning method based on definition and luminance evaluation. The rapid license-plate positioning method includes the following steps: 1, carrying out definition evaluation based on noise and sharpness on an input image; 2, carrying out gradient sharpening processing if the definition is insufficient, and carrying out Gaussian fuzzy processing if the definition is excessively high; 3, converting the RGB image into a grey-scale image; 4, carrying out luminance evaluation on the grey-scale image; 5, carrying out illumination unification processing if the luminance is abnormal; 6, extracting the perpendicular edges in the image through a Scharr operator; 7, carrying out local self-adaption threshold processing; 8, filtering the noise through morphological processing, and fusing the perpendicular edges to form a communicated area; 9, carrying out area marking on the communicated area, carrying out screening according to the characteristics of Chinese license plates, and obtaining a license plate area.

The invention provides a natural background video sectional drawing method. The method comprises the steps that 1) the method automatically selects a key frame, specifically the method automatically selects the key frame in a video by calculating inter-frame difference; 2) a key frame Trimap map is generated, specifically a user us allowed to provide useful information through a small amount of manual interaction and a GrabCut segmentation algorithm and a Gaussian blur method are used to semi-automatically generate the Trimap map on the key frame; 3) a middle frame Trimap map is generated, specifically bidirectional optical flow propagation is used to generate the Trimap map of each middle frame, and color, gradient and other information are used to correct errors generated by optical flow propagation; and 4) according to the Trimap maps of all video frames and a Bayesian sectional drawing algorithm with added three-dimensional space-time optimization, the alpha mask map of each frame of the video is calculated, and the alpha mask map and a new background are synthesized to acquire a new synthesized video. According to the invention, the method has the advantages of fast speed and less user interaction, and the finally acquired new synthesized video has the advantages of good time coherence, high reality and the like.

The invention discloses a method for improving image definition in foggy days based on an imaging model. The method for improving the image definition in foggy days based on the imaging model includes the following steps that firstly, an RGB image of original misty days is converted into a YUV color space and three-grade wavelet decomposition is carried out on the luminance Y component; a misty day imaging model is combined on a low-frequency sub-band of a wavelet zone, Gaussian Blur is used for estimating and removing scattered light of media, attenuation factors are adjusted with a method based on local complexity and self-adaption enhancement is performed on an attenuation low-frequency sub-image; a non-linear conversion enhancement method is utilized on a high-frequency sub-band to further enhance high-frequency information; finally, a sharpened colored image is obtained through wavelet inverse conversion and conversion of the color space. According to the method for improving the image definition in foggy days based on the imaging model, the problem of blurring caused by atmospheric scattering can be rapidly and effectively solved, the definition and the contrast ratio of the foggy day image are effectively improved and high real-time performance is achieved.

The invention discloses an electronic image stabilizing method and an electronic image stabilizing system aiming at moving object detection under camera shake. The method comprises the following steps: carrying out guassian blur on the original image, lowering the resolution ratio and determining three layers of images; determining a first translational motion vector corresponding to an image with the lowest resolution ratio positioned at the highest layer based on a blocking gray projection method; compensating an interlayer image based on the acquired first translational motion vector; determining a second translational motion vector corresponding to the image positioned in the interlayer based on a block match algorithm; compensating the original image with the highest resolution ratiopositioned at the lowest layer based on the first translational motion vector and the second translational motion vector; and smoothing and compensating the motion by utilizing a feature matching method. The method and system provided by the invention have good robustness to the dynamic background, the noise, and the consistency of motion, rotation and background of large-sized images, have superiority on the image quality, the visual effect and the calculation speed, and satisfy the requirements of the moving object detection under the camera shake after the image is stabilized.

The invention discloses a device and a method for virtually processing a depth image. The device comprises an obtaining module used for obtaining an aperture value, a focal length, a focusing distance and a permissible confusion circle diameter; a calculating module used for calculating a field depth range according to the obtained aperture value, focal length, focusing distance and permissible confusion circle diameter; a determining module used for obtaining object distances to a focus and determining virtual degrees corresponding to the different object distances according to the obtained object distances to the focus, aperture value and focusing distance; a correction module used for correcting radiuses corresponding to Gaussian blur according to the determined virtual degrees corresponding to the different object distances; and a fuzzy processing module which is used for carrying out fuzzy processing on pixels of the depth image requiring virtual processing and out of the field depth range according to the corrected radiuses corresponding to the Gaussian blur and is used for shooting. According to the technical scheme provided by the invention, the shooting effect is improved without changing a physical hardware structure of an existing mobile terminal.

The invention provides a facial image layer decomposition method based on an improved guide filter, and the method comprises the steps: carries out the improved guide filtering of a facial image brightness channel, obtaining a human face structure information layer, further carrying out differential operation, and obtaining a texture detail layer. According to the invention, the method carries out the regularization factor adaptive adjustment optimization of a guide filter and Gaussian blurring and corrosion processing of a parameter matrix image, and obtains different filtering effects of a facial image in different regions. The method is used for a digital makeup system, obtains an excellent layer decomposition effect, and remarkably reduces the time complexity of layer decomposition.

The invention provides a video content replacement method and system based on an adversarial generative network. The method comprises the following steps: extracting a source target image in a video frame; performing semantic segmentation on the source target image; performing data enhancement on the source target image by using the image transformation operation; inputting the semantic segmentation images paired by the enhanced data set into a network training generative adversarial model; accurately detecting an ROI region of the source target image and carrying out cutting and straightening; taking the ROI image as a generation model input to obtain a generated target image and a synthetic mask; controlling edge smoothing and deblurring of the generated target image by using mask control after Gaussian blur; adjusting the hue of the generated target image to be consistent with that of the frame source target image by a histogram matching strategy; generating video frame anti-jitterprocessing; and fusing the source image and generating a target image. Compared with the prior art, the method and system has the characteristics and advantages of easiness in operation, low cost, good effect, short consumed time and the like.

The invention provides a self-adaptive image scaling method based on bicubic interpolation. The self-adaptive image method based on the bicubic interpolation comprises the following steps: conducting a Gaussian blur operation of a source image so that detail information which can not be displayed under the condition of low resolution is eliminated and distortion of a scaled image is avoided; finding the corresponding pixel location of a target image pixel point on the source image, self-adaptively selecting m sampling pixel points which are corresponding to the periphery of pixel points, obtaining a weighted value which is corresponding to each sampling pixel point according to a bicubic interpolation function, and weighting and summing and to get a pixel value of the scaled image according to the weighted values of the m sampling pixel points and pixel values of the m sampling pixel values; and conducting a sharpening operation of the scaled image so that the edges of an image are clear and a target image is obtained.

The invention discloses a camera wide dynamic image enhancement method and a device. Pixel mean values of a bright pixel area and a dark pixel area of images in original image data are respectively and statistically figured out, brightness increase factors are obtained by means of different value ranges of the pixel mean values, the brightness increase factors are used as parameters and an index curve is used so that original images are mapped once to obtain stretched images, the stretched images are subjected to gaussian blur processing to obtain low frequency image spectrum of the stretched images, and a specific value of the low frequency image spectrum to the stretched images is used as a parameter to perform contrast ratio improvement for the stretched images so as to finally obtain wide dynamic images.

The invention relates to the technical field of camera focusing, in particular to an automatic focusing method and device for a camera based on a defocus estimation improved hill climbing method. Thedefocus state of the camera can be regarded as a Gaussian blur state of an image. When the camera is in a quasi-focal state, the original image of a current frame image deviates greatly from a definition evaluation value of the Gaussian blurred image; when the camera is in the defocus state, the original image of the current frame image deviates little from the definition evaluation value of the Gaussian blurred image, and if the defocus depth is larger, the deviation of the definition evaluation value is smaller. Thus, the defocus depth of the current frame image can be expressed by calculating the deviation between the original image of the current frame image and the Gaussian blurred image of the current frame image, and the focusing step of the camera is adaptively adjusted according to the defocus depth, so that the quasi-focus position can be quickly arrived at on the premise that the focusing precision is ensured, and the problem that the speed and the precision of the camera are difficult to balance during focusing is solved.

The invention relates to an image enhancement method achieved by intelligently increasing the field depth. The method includes the steps of obtaining a practical body area by marking an original image to obtain a marked area and by conducting outward judgment on the boundary of the marked area, then, generating a masking layer on the marked area or the body area, and finally conducting mixed calculation on the original image and a Gaussian blur image according to the generated masking layer to obtain a result image with the intelligently-increased field depth. Therefore, intelligent field depth increase can be rapidly and accurately conducted on the image, and the effect can be more natural.

The invention discloses an image depth extraction method, which comprises the following steps of: (1) performing Gaussian fuzzy processing on an original image to be processed to obtain N fuzzy images, wherein N is more than or equal to 2; (2) detecting the edges of the original image and the N fuzzy images to obtain an edge image of each image; (3) calculating the corresponding fuzzy parameter estimation value of each pixel point under each Gaussian filtering parameter on the edges based on the edge images; (4) analyzing N fuzzy parameter estimation values of each pixel point on the edges by using a statistical method to obtain an optimal fuzzy parameter estimation value of each pixel point on the edges; (5) calculating the depth value of each pixel point on the edges in the images according to the optimal fuzzy parameter estimation value of each pixel point on the edges to obtain a sparse depth map; and (6) performing interpolation processing on the sparse depth map to obtain a dense depth map. Compared with a method in the prior art, the method has the advantages that high-accuracy fuzzy parameters can be obtained, so that a depth value obtained through subsequent calculation has high accuracy.

The invention belongs to the field of digital image processing and discloses a method for automatically removing image black eyes. The method is characterized by comprising the following steps: first drawing the contour regions of a left eye bag and a right eye bag and marking as eye bag contour pictures; then, obtaining a face region picture; extracting the brightness of each pixel of the face region, leading into a result and marking as an image B; sequentially carrying out average filtering, high contrast reservation and brightening algorithms on the image B to obtain an image C; sequentially carrying out average filtering and histogram statistics on the image B, then calculating the image B with the eye bag contour pictures and the image C to obtain an image D, and carrying out Gaussian blurring on the image D; carrying out Gaussian blurring on the face region to obtain a face Gaussian picture; finally calculating the image D, the face region and the face Gaussian picture to obtain a result picture. The technical scheme realizes the function of automatically removing black eyes, reduces user operation difficulty and threshold to the lowest, can be popularized in various portable digital devices, saves man-machine interaction devices such as a mouse and a keyboard, is low in hardware cost and has the characteristics of convenience and rapidness.

The invention discloses an image processing method, system and shooting terminal for optimizing edge aliasing. The method herein comprises the following steps: conducting smooth processing on a contour of which an image is to be processed to obtain a smooth closed contour curve, using the closed contour curve to conduct morphological reconstruction and Gaussian Blur processing on the image to be processed to obtain a fuzzy image of a binary image, then conducting a one-time gray scale statistics and threshold value processing on all the edges of the fuzzy image to obtain a first gray scale mapping table, and in accordance with the first gray scale mapping table, calculating so as to obtain a gray scale image, and further conducting a second gray scale statistics and threshold value processing on the aliasing edge of the gray scale image to obtain a second gray scale mapping table, and in accordance with the second gray scale mapping table, adjusting the aliasing edge to obtain an anti-aliasing image. According to the invention, the algorithm is simple, the operation speed is fast, and the improvement on the aliasing edge is explicit, and the application range is wide.

The invention belongs to the image processing field and includes a face key point-based automatic under-eye bag removing method and system. The method includes the following steps that: an input image is obtained; the masking-out image and under-eye bag area image of the under-eye bag area of the input image are extracted; the under-eye bag area image is transformed to an LAB color space, and a color channel L, a color channel A and a color channel B are separated out; a brightness adjustment curve is obtained through calculation based on the channel L; the channel L is mapped by the brightness adjustment curve, so that a new channel L is obtained; the new channel L, the channel A and the channel B are converted into an RGB color space image; Gaussian blur is performed on the masking-out image of the under-eye bag area, so that a new masking-out image can be obtained; the under-eye bag area image, the RGB color space image and the new masking-out image are composited, so that a new under-eye bag area image can be obtained; and the under-eye bag area image is replaced by the new under-eye bag area image, so that a final image can be obtained. With the face key point-based automatic under-eye bag removing method and system of the invention adopted, the removal of under-eye bags can be completed through one-key operation. The method is simple and stable, and the effect of an image processed by using the method is natural.