113 results about "Laplacian pyramid" patented technology

A computer implemented method for fusing images taken by a plurality of cameras is disclosed, comprising the steps of: receiving a plurality of images of the same scene taken by the plurality of cameras; generating Laplacian pyramid images for each source image of the plurality of images; applying contrast normalization to the Laplacian pyramids images; performing pixel-level fusion on the Laplacian pyramid images based on a local salience measure that reduces aliasing artifacts to produce one salience-selected Laplacian pyramid image for each pyramid level; and combining the salience-selected Laplacian pyramid images into a fused image. Applying contrast normalization further comprises, for each Laplacian image at a given level: obtaining an energy image from the Laplacian image; determining a gain factor that is based on at least the energy image and a target contrast; and multiplying the Laplacian image by a gain factor to produce a normalized Laplacian image.

A multimedia compression system for generating frame rate scaleable data in the case of universally scaleable data. Universally scaleable data is scaleable across all of the relevant characteristics of the data (e.g., frame rate, resolution, and quality for video). The scaleable data generated by the compression system includes multiple additive layers for each characteristic across which the data is scaleable. For video, the frame rate layers are additive temporal layers, the resolution layers are additive base and enhancement layers, and the quality layers are additive index planes of embedded codes. Various techniques can be used for generating these layers (e.g., Laplacian pyramid decomposition or wavelet decomposition for generating the resolution layers; tree structured vector quantization or tree structured scalar quantization for generating the quality layers). The system further provides for embedded inter-frame compression in the context of frame rate scalability, and non-redundant layered multicast network delivery of the scaleable data.

The invention discloses an image registration and multi-resolution fusion-based panoramic image splicing method. An adaptive threshold of sequence images is subjected to binary processing and image contour information is extracted by utilizing a pixel neighborhood relationship; a similar region of an image contour structure is quickly estimated through a similarity measurement method for Hu momentinvariant features; and SIFT feature point extraction is performed in a similar region part of two original images. Moreover, feature point initial matching is performed by using a bidirectional K-nearest classification algorithm; mismatched points are screened by an RANSAC algorithm; and an affine transformation matrix among the images is estimated. In addition, in an image fusion aspect, brightness equalization and local color mapping processing is adopted. Then, through an improved Laplacian pyramid multi-resolution fusion algorithm, spliced images are fused to realize seamless splicing. According to the method, the image splicing time is shortened, the image splicing efficiency is improved, the similar region can be quickly located, the mismatch in splicing is effectively improved, and the image registration precision is improved.

The invention discloses a double Laplacian pyramid and convolutional neural network-based image amplification method. The method comprises the steps of obtaining a low-resolution image, and performingbicubic amplification processing to obtain a blurred high-resolution image with a corresponding amplification factor; performing feature extraction processing on the blurred high-resolution image toobtain image features of different scales from large to small; performing feature extraction processing on a low-resolution image to obtain image features of different scales from small to large in layers; and fusing the obtained image features of the different scales from large to small and image features of the different scales from small to large in the layers by reconstructing a super-resolution network to obtain a high-resolution image. By implementing the method, the high-quality high-resolution image can be reconstructed more quickly; the image quality is improved by reconstructing thelow-resolution image; and therefore, the method can be better applied to more scenes.

A method, system and computer readable medium for implementing and performing a multi-resolution contrast-based dynamic range management algorithm and allowing for the efficient compression of an intensity dynamic range of an input image to a reduced intensity dynamic range of an image display signal. Specifically, the method, system and computer readable medium of the invention comprise decomposing the input image into a plurality of image components, modifying the intensity characteristics of the plurality of image components and reconstructing the plurality of image components into an output image for display on an image display device. In another aspect of the method, system and computer readable medium of the invention, the decomposition of the input image and reconstruction into an output image are performed using a Laplacian pyramid. In a further aspect of the method, system and computer readable medium of the invention, the intensity characteristics of each of the image components in the plurality are modified separately.

The present invention is to provide an image processing apparatus using the difference among scaled images as a layered image and a method thereof, which utilize the Gaussian and Laplacian pyramid theory to convert an original image into a plurality of scaled images of different scales, and the difference among scaled images of two adjacent different scales as a layered image of the corresponding layer, so that the edge and line characteristics of a scene of the original image for each layered image can be displayed in different levels sequentially from a clear level to a vague level, and provide a layered image display interface and an image characteristic editing interface for users to examine each layered image through the layered image display interface and edit or perform special effect to each layered image, so as to simulate different visual effects based on different vision models.

The invention discloses a foggy image sharpening method and device, and a storage medium. The method comprises the steps of obtaining a foggy image data set and a fogless image data set; constructinga cyclic generative adversarial network and a perception loss network, wherein the cyclic generative adversarial network comprises a first generator, a second generator, a first discriminator and a second discriminator; inputting the foggy image data set and the fogless image data set into a cyclic generative adversarial network, and training in combination with the perception loss network to obtain an optimal generation model; inputting the image to be defogged into the optimal generation model to obtain a corresponding predicted fog-free image; and performing Laplace pyramid reduction on thepredicted fog-free image to obtain a clear fog-free image. According to the method, the characteristics of the foggy image can be automatically extracted, style conversion of a single foggy image anda single fogless image is completed, and the foggy image and the real fogless image which are matched in pairs in the same scene do not need to be obtained for training, so that the foggy image is conveniently and flexibly clarified.

The invention discloses an X-ray image enhancement method based on an image pyramid. The method mainly comprises the steps of image segmentation, Gaussian pyramid and Laplacian pyramid construction, image detail enhancement, image reconstruction fusion, background restoration and the like. The method has the advantages that on the premise that the background of the X-ray image is not changed, thecontrast ratio of a black area with small image gray value difference can be remarkably improved, and the outline of an object in the image can be clearly seen.

The invention relates to an image enhancement method, a terminal device and a storage medium. The method comprises: starting from incident component estimation, performing convolution operation on animproved bilateral filtering function and an input image to estimate an incident light component, so that edge signals of the image are reserved, and a filtering effect is achieved; performing downsampling by using a Gaussian-Laplace pyramid to obtain images of different scales of the filtered image, performing image enhancement operation on the images of different scales respectively, and finallyperforming reconstruction through the Gaussian-Laplace pyramid to obtain a final enhanced image. According to the invention, the image contrast can be effectively enhanced, and the color of the processed image conforms to the human eye observation effect.

The invention discloses a method and a system for processing color noise in an image, electronic equipment and a storage medium. The method comprises the following steps: acquiring original channel data; obtaining decomposed image data of the L decomposition layers; calculating the similarity between to-be-processed pixels and neighborhood pixels of the YUV three-channel data in the to-be-processed data of the Lth layer, determining the joint weight of each neighborhood pixel position, and denoising the UV channel according to the neighborhood pixels of the UV channel and the joint weights atthe neighborhood pixel positions; and performing up-sampling on the denoising result of the Lth layer, combining the denoising result of the Lth layer with the Laplace pyramid data of the L1th layer to obtain data of the L1th layer, performing operation similar to that of the Lth layer on the data, performing circulation in sequence until the data of the first layer is obtained, and performing filtering and denoising on the data to finally obtain denoised image data. According to the invention, large-size color noise and small-particle noise points can be suppressed at the same time, the coloredge retention characteristic is good, and the color overflow phenomenon can be effectively reduced.

The invention provides a tropical cyclone intensity objective determination method based on a satellite cloud chart and an RVM. The method is used for constructing a tropical cyclone (TC) intensity objective determination model based on the satellite cloud chart and the RVM (relevance vector machine). The method mainly comprises the following two aspects: 1) carrying out fusion on infrared and water vapor channel cloud charts by utilizing a Laplacian pyramid algorithm, constructing a deviation angle-gradient co-occurrence matrix with the TC center as a reference point, constructing characteristic factors closely related to TC intensity by utilizing a plurality of statistical parameters in the co-occurrence matrix and information of TC kernel scale and center latitude and the like, and establishing the TC intensity objective determination model by utilizing the RVM; and 2) based on the fused satellite cloud chart, and with each point as the reference point in sequence, constructing a deviation angle-gradient co-occurrence matrix and calculating a minimum value, a median value and a mean value of a co-occurrence matrix statistical parameter array. The method constructs the characteristic factors closely related to TC intensity by utilizing the plurality of statistical parameters of the co-occurrence matrix parameter array and information of TC kernel scale and center latitude and the like, and establishes the TC intensity objective determination model by utilizing the RVM.

An embodiment of the invention discloses an image processing method, intelligent equipment and a computer readable storage medium. The image processing method comprises the steps of: acquiring an original image to be subjected to moire processing; and running an image processing model to carry out moire removal processing on the original image to obtain a target image, wherein the image processingmodel is a network model which is obtained by training in advance according to a moire training data set and can remove moire included in the image. The image processing model comprises a multi-bandmodule, wherein the multi-band module is used for processing the original image to obtain N layers of Laplace pyramids about the original image, the multi-band module obtains a first processing resultfeature map based on feature maps corresponding to N spatial frequency bands of N layers of Laplacian pyramids, the target image is obtained according to the first processing result feature map, andN is a positive integer greater than or equal to 2. By adopting the image processing method, the intelligent equipment and the computer readable storage medium, a better moire removing effect can be achieved.

The invention discloses an image super-resolution reconstruction method based on a multi-scale pyramid network. The image super-resolution reconstruction method comprises the following steps of S1, performing shallow feature extraction on an input image; S2, performing feature fusion and feature enhancement on the shallow features through K multi-scale residual modules to obtain richer deep features; S3, performing up-sampling on the deep features by using transposed convolution; S4, reconstructing the image by using residual learning; and S5, taking the reconstructed image as the output of the current pyramid network and the input of the next layer of pyramid network, and continuing to adopt the steps S1-S4 for training, so as to obtain an image with higher resolution. According to the method, a multi-scale residual module is adopted to fuse features to obtain richer features; meanwhile, a Laplacian pyramid network is adopted to gradually upsample and reconstruct a high-resolution image; and by means of the method, images with richer details and higher quality can be reconstructed.

The invention discloses an underwater image splicing method and device, and belongs to the technical field of image processing. The method comprises the following steps: acquiring at least two underwater images to be spliced; sequentially performing red channel attenuation compensation processing and white balance processing on each underwater image to obtain a preprocessed underwater image corresponding to each underwater image; performing image enhancement processing on each preprocessed underwater image based on the multilayer image pyramid to obtain an enhanced underwater image corresponding to each preprocessed underwater image; performing image registration processing on the at least two enhanced underwater images based on grid optimization to obtain registered underwater images; performing image fusion processing on the registered underwater images based on a Laplace pyramid multi-scale image fusion algorithm to obtain a fused underwater image; and determining that the fused underwater image is a spliced underwater image, and outputting the spliced underwater image. The invention realizes underwater image splicing method.

The invention discloses a multi-resolution deep network image highlight removing method based on divide-and-conquer. The method comprises a training method and a testing method. Firstly, a highlight network removing model is constructed, and the model is composed of a pyramid structure, a nested residual network and a fusion structure. A pyramid structure uses a Laplace pyramid to grade image blocks, highlight is processed on different levels, a convolutional network and a residual network are adopted in a nested residual network to extract features of the image blocks of different levels, anda fusion structure is combined with output of the nested residual network to predict a non-highlight image. And after model training is completed, directly partitioning the test image, predicting a non-highlight image by the model, and finally splicing prediction results to obtain a non-highlight whole image. According to the model structure, the highlight phenomenon in the image can be efficiently removed in real time, and the model structure has high adaptability and high robustness to images with complex colors and textures.

The invention discloses a fire-fighting unmanned aerial vehicle image fusion method based on visible light and infrared thermal imaging. The method comprises the steps: using a visible light camera and an infrared thermal imaging camera of a fire-fighting unmanned aerial vehicle to capture a visible light image and an infrared thermal image respectively; converting the two pictures into an image with the size of 1280 * 720 through a size function in the OpenCV; extracting ROI vertex coordinates in the infrared thermogram and displaying the ROI vertex coordinates in the visible light image by using a red rectangular frame; fusing an image in the edge obtained in the infrared thermogram and an image of a coordinate corresponding to the visible light by using a Laplace pyramid algorithm to obtain fire point information; and after the fire point is detected, calculating the three-dimensional position of the target according to the visual information, and accurately estimating the three-dimensional position information of the fire point under the coordinate system of the fire-fighting unmanned aerial vehicle by calculating the relative height and yaw angle between the unmanned aerial vehicle and the target. The method has the advantages of being simple in structure, convenient to operate and easy to implement.

The invention discloses a vehicle type automatic recognition method and system. The method comprises: obtaining a vehicle roof characteristic acquired by a laser radar; obtaining a first image acquired by a first camera; obtaining a second image acquired by a second camera; determining a transformation matrix between the first image and the second image; splicing the first image and the second image according to the transformation matrix; performing image fusion on the spliced images by employing an image fusion algorithm based on a Laplacian pyramid; performing smooth processing on the fusion image; extracting a vehicle chassis characteristic according to the smoothed image; extracting a vehicle wheel shaft characteristic according to the smoothed image; and determining the vehicle type according to the vehicle chassis characteristic, the vehicle wheel shaft characteristic and the vehicle roof characteristic. According to the vehicle type automatic recognition method and system, the requirement of the installation precision is low, the calculation amount is small, and the accuracy of vehicle type recognition is high.

An input image is divided into images composed of luminance signals of a plurality of bands that make up the Laplacian pyramid, noise suppression is applied to each divided image, and then the divided images are composited by addition. The input image is also divided into images composed of color signals of a plurality of bands that make up the Gaussian pyramid, noise suppression is applied to each divided image, and the divided images are composited at an image-based ratio. By thus compositing the luminance signals and color signals, excellent noise suppression can be realized while alleviating deterioration in the image quality during the compositing.

The invention discloses a multi-exposure image fusion method for removing ghosting in a dynamic scene. The method comprises the following steps: firstly, selecting a proper exposure image in an exposure sequence as a reference image, then calculating a relative exposure weight based on gradient direction information of pixels, removing abnormal dynamic pixels and ghost pixels through dynamic pixel correction, and finally, performing fusing by using a six-layer image Laplacian pyramid to obtain a ghosting-free fused image. According to the method, the dynamic fusion requirements under various conditions can be effectively met, and the ghosting trouble possibly caused by a moving object in the exposure fusion process is avoided, so that the efficient multiple exposure fusion in a dynamic scene is achieved.

The invention relates to a remote sensing image space-time fusion method and system. The method comprises the following steps: resampling a high-resolution image in a known period to twice the spatial resolution of a low-resolution image in the known period, and constructing a Laplacian pyramid network comprising two layers; based on a Laplacian pyramid network, determining a sudden change prediction result and a regression coefficient; applying the regression coefficient to the high-resolution image in the known period to obtain a preliminary time change prediction result; correcting the preliminary time change prediction result by using similar pixels in a neighborhood, and determining a gradual change prediction result; performing weighted average processing on the sudden change prediction result and the gradual change prediction result by using a linear weighting method based on a sliding window, and determining a final time-space fusion result, wherein the final space-time fusion result is a high-resolution image in the prediction period.

The invention relates to an image subjective visual effect enhancement method based on a Laplace pyramid, and belongs to the field of digital image processing. The method comprises the steps of 1, performing Laplace pyramid decomposition on an original image to obtain an approximate image and a multi-scale detail image; and 2, enhancing the approximate image based on an improved contrast limited adaptive histogram equalization algorithm; 3, enhancing the multi-scale detail image; and 4, performing Laplace inverse transformation on the enhanced approximate image and the multi-scale detail imageto obtain an enhanced image. Through the steps, the problems of boundary effect, over-enhancement, overlarge noise and the like possibly occurring in the image after traditional contrast-limited adaptive histogram equalization enhancement can be effectively relieved, and the subjective visual effect of the image is remarkably improved.

The invention discloses a self-adaptive exposure adjustment multi-scale entropy fusion underwater image enhancement method, which comprises the steps of applying generalized bounded logarithm additionor subtraction operation to an original underwater image to derive derivative images with different exposures; calculating multi-scale Gaussian pyramid images with different exposure quantities and multi-scale Laplace pyramid images with different exposure quantities; calculating multi-scale Gaussian pyramid weight images with different exposures; obtaining a normalized multi-scale Gaussian pyramid weight iteration image with different exposure quantities; obtaining a multi-scale pyramid fusion image; obtaining a pyramid fusion image; and obtaining an enhanced image based on rich gradient information of the pyramid fusion image. According to the method, the underwater image is enhanced, so that the contrast and saturation of the enhanced image can be well considered, image details are richer and clearer, and the contrast, information entropy, color information and the like of the whole image can be further improved.

The invention relates to a quality evaluation method for a multi-exposure fusion image simultaneously for a dynamic scene and a static scene. The quality evaluation method is characterized by comprising the steps: firstly dividing a fusion image into a region with a larger information amount and a region with a smaller information amount through employing a superpixel segmentation method based onthe structural difference between the multi-exposure fusion image and a source exposure-level sequence image; secondly, based on a Laplace pyramid, respectively calculating quality maps of regions with relatively large information amount and regions with relatively small information amount; and thirdly, calculating the local quality scores of the regions with large information amount and small information amount by adopting an adaptive weighting strategy based on the information theory, and averaging the obtained local quality scores of the regions with large change and small change to obtainthe quality score of the fused image. Experimental results show that the quality evaluation method shows good prediction performance for visual quality evaluation of multi-exposure fusion images of adynamic scene and a static scene.

The invention discloses a landslide mass recognition method based on Laplacian pyramid remote sensing image fusion. The method comprises the steps: reconstructing an original remote sensing image through a Laplacian pyramid fusion module according to the extracted local features and global features of the remote sensing image, and generating a fusion image; constructing a deep learning semantic segmentation model through a semantic segmentation network; then respectively marking places where landslide disasters occur and places where landslide disasters do not occur in the fused image through a picture marking tool to obtain a landslide disaster tag map data set; and finally, training a deep learning semantic segmentation model by using the data set, and storing the model by modifying a semantic segmentation network structure and adjusting model parameters until a loss curve of the model reaches fitting and when the precision of recognizing the landslide mass in the remote sensing image meets the requirement. According to the method, an image fusion model based on the Laplacian pyramid is combined, and an effective decision basis can be efficiently and accurately provided for disaster prevention and reduction of landslide disasters.

The invention provides a texture mapping method in three-dimensional virtual human head and face modeling, and the method comprises the steps: cutting a front view and a side view of a real human face of a to-be-mapped person according to the position of a feature point through building a personalized human face model, and calculating the offset and the offset angle of each flush region; performing shear transformation on the cut side view according to the offset and the offset angle to obtain a side view after shear transformation; and introducing a Laplacian pyramid algorithm to fuse the mirror image of the cut front view, the mirror image of the side view after the shear transformation and the mirror image of the side view after the shear transformation, and mapping the fused image into the personalized face model to obtain a texture mapping face model of the to-be-mapped figure. Texture cracks can be effectively reduced under the condition of ensuring low data acquisition cost, so that the mapped texture mapping face model is more vivid.

A medical image super-resolution reconstruction method based on network cascading comprises the following steps that: 1) an image is preprocessed, so that the image can better participate in training,and learning of a subsequent network is facilitated; 2) an image pyramid is constructed, and cascade information of the images are extracted; 3) super-division is carried out on the image by utilizing a cascade network; 4) the design basis of the cascade network is a Laplace pyramid and a BE-GAN, and the network generates a high-definition super-resolution image by generating a residual image andadding the residual image to an original image; the total amount of information required by network learning can be reduced by using the residual image, and the training pressure of the network can be reduced, so that the network can obtain a better effect under a smaller data set; cascading enables all levels of the network to be relatively independent, so that the network can adapt to super-division tasks under all resolutions.

The invention discloses a multi-exposure image ghosting-free fusion method under global gradient based on patch alignment, which comprises the following steps: reading a reference image, measuring the similarity between the reference image and an LDR image based on a multi-source bidirectional similarity measurement algorithm MBDS, and aligning a motion area in the LDR image by adopting a patch acceleration method; adopting a reconstruction algorithm to obtain an LDR image sequence aligned with the reference image; designing a pixel relative intensity weight formula and a global gradient weight formula; and carrying out weighted averaging on the two weight expressions to obtain a final weight expression, inputting the weight map and the LDR image sequence in the Laplacian pyramid to carry out image fusion, and outputting a fused image. According to the method, the problem of artifacts occurring under dynamic scene fusion is effectively solved, the LDR image is registered based on the reference image, the fusion time is saved, the robustness is higher, then multi-scale decomposition fusion is carried out in the Laplacian pyramid, the fusion effect is better, and the obtained HDR image is rich in detail information and better in visual effect.

The invention relates to an MRI image fusion method based on Laplace pyramid transformation and MRI equipment. The MRI image fusion method comprises the following steps: S1, for the features of a brain MRI image, decomposing a plurality of source images by using Laplace pyramid decomposition to obtain different frequency layers, and adopting different fusion rules in the different frequency layersso as to reserve feature information of each source image in the different frequency layers in a fused image; S2, respectively calculating the regional mean value of the top layer and the point sharpness of other layers to serve as fusion scales; S3, performing normalization processing on the regional mean value and the point definition; S4, comparing the normalized region mean value and point definition value of each layer of different source images, and obtaining a fusion result of each layer of images by adopting different fusion strategies; and S5, for each layer of the Laplace pyramid ofthe obtained fusion image, performing recursion downwards layer by layer from the top layer, and finally obtaining the fusion image. By adopting the MRI image fusion method, the multi-focus fusion image with low noise and clear edge can be obtained.

The invention discloses a multi-exposure image fusion method and system and a storable medium, and belongs to the technical field of image processing, and the method comprises the steps: inputting an image, calculating a good exposure estimation weight and a chroma weight, and carrying out the convolution of a Gaussian pyramid of a comprehensive weight map and a Laplacian pyramid of the input image, and obtaining an initial fusion pyramid; secondly, carrying out local detail enhancement on the obtained brightest and darkest regions of interest by utilizing a fast local Laplacian filter (FLLF), then taking the maximum value of Laplacian coefficients to obtain a maximum coefficient Laplacian pyramid, updating the coefficients in the regions of interest into an initial fusion pyramid, and obtaining a maximum coefficient Laplacian pyramid; according to the method, under the condition that it is guaranteed that information is not lost in the brightest and darkest areas, proper enhancement can be carried out, the naturalness of light and shadow transition is guaranteed, and image details are clearer.