34 results about "Camera response function" patented technology

A new high dynamic range image synthesis which can handle the local object motion, wherein an interactive graphical user interface is provided for the end user, through which one can specify the source image for separate part of the final high dynamic range image, either by creating a image mask or scribble on the image. The high dynamic range image synthesis includes the following steps: capturing low dynamic range images with different exposures; registering the low dynamic range images; estimating camera response function; converting the low dynamic range images to temporary radiance images using estimated camera response function; and fusing the temporary radiance images into a single high dynamic range (HDR) image by employing a method of layered masking.

The invention discloses an IMU information-based wide dynamic image fusion method. According to the method, different shutter time is set, the same scene is subjected to multiple times of long exposure and short exposure, and then three frames of images of which bright and dark area contrasts are relatively large are selected, including the shortest exposure image frame, the moderate exposure image frame, and the longest exposure image frame; the three frames of images are divided into M areas respectively, each area is subjected to Euler angle multi-data fusion attitude calculation based on information detected by an IMU sensor to obtain a motion vector of each area, and a motion vector mean is calculated to be used as a correcting value to perform registration correction on each image frame; and the different exposure images that are corrected are subjected to segmented linear fitting by adoption of the least square method in combination with a camera response function and a luminance mapping function, then wide dynamic images are obtained through mapping, and finally, the wide dynamic images are fused by a multi-frame accumulation method. Through adoption of the method, a phenomenon that a transition area generated by direct fusion of two frames of images is unnatural is optimized, and thus the clear and natural wide dynamic images are obtained.

The invention discloses a multi-exposure image deghosting integration method based on low-rank matrix recovery. First of all, a multi-exposure image sequence is input in a normalization mode; then, radiation calibration is performed on a normalized image by use of a camera response function; then the multi-exposure image sequence is quantified so as to form a data matrix recovered from a low-rank matrix; the low-rank matrix is obtained by use of an improved low-rank matrix recovery algorithm; and an high dynamic range (HDR) image of a target is recovered from low-rank matrix data. According to the invention, by use of a latest research result of low-rank matrix recovery, the problem of effectively removing artifacts and fuzziness in the integrated HDR image can be solved.

The invention discloses a high-dynamic-range image synthesis method and device, and the method comprises the steps: obtaining at least two image frames with different exposures or a plurality of image frames at a high frame frequency at the same scene, and obtaining the gray values of all pixels of each image frame; employing the gray values to replace the illuminance according to a classical HDR image synthesis formula, and forming a first HDR image synthesis formula; optimizing the first HDR image synthesis formula, and generating a second HDR image synthesis formula; synthesizing an HDR image through the second HDR image synthesis formula; representing the generated HDR image through a segmented linear model, and enabling the generated HDR image to be displayed on common display equipment. Through simplifying a camera response function and simplifying the HDR image synthesis formula in the HDR synthesis and the optimization process of compression mapping, the method saves a lot of logic and storage resources, thereby improving the implementability of hardware FPGA, and meeting the high-speed and real-time requirements of the hardware equipment.

The invention discloses a method for modeling a background based on a camera response function in an automatic gain scene, which comprises the following steps of: performing automatic gain progressiveness-based analysis to obtain a roughly divided background area, obtaining low-noise training data by using a joint histogram method, and performing recovery once to obtain a globally optimal camera response function by the method based on maximum likelihood estimation and parameter constraints; online calculating a gain ratio frame by frame by utilizing correlation between a foreground and background difference and the gain ratio and the homography of a grayscale difference function relative to the gain ratio; and if the gain ratio is not 1, performing updating to obtain a background reference frame the same as a current reference frame by using the camera response function and the gain ratio, otherwise determining the background reference frame is unchanged, and obtaining the background reference frame with a gain coefficient the same as that of the current frame along with the change of the gain coefficient of a camera. By the method, the shortcomings of high background change speed, caused by difficulties in realizing automatic gain along with the camera, of the conventional methods are overcome, thereby ensuring high-efficiency motion detection.

The invention discloses a minimum bracketing exposure set acquisition method based on optimal exposure. A technical problem that a high-dynamic range image synthesized by using the existing minimum bracketing exposure acquisition method is bad in imaging quality is solved. The technical scheme is follows: orderly acquiring irradiance ranges under different exposures by adopting a Debevec&Malik camera response function acquisition method, establishing a relevant exposure corresponding relation between the target scene optimal exposure and the camera captive exposure, and traversing the camera standard exposure sequences to obtain the minimum bracketing exposure image set corresponding to the target scene in the camera standard exposure sequence; computing the minimum bracketing exposure image set based on the target scene optimal exposure according to the exposure step difference by the optimal exposure time of the target scene, wherein the obtained exposure set comprises the optimal exposure containing most available information of the target scene. The image quality is guaranteed, and the redundancy time in the exposure time and the total consumed time for capturing image set are effectively reduced.

The invention relates to an image processing method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a first image and a second image to be processed, wherein the first image is shot by a first camera, and the second image is shot by a second camera; performing pixel mapping on the second image based on a pixel mapping relationship betweenthe first camera and the second camera to obtain a mapping image corresponding to the second image, wherein the pixel mapping relationship is determined based on a first camera response function of the first camera and a second camera response function of the second camera; and aligning the mapping image corresponding to the second image with the first image. By adopting the method, the image alignment effect can be improved.

A method for estimating image conversion parameters is revealed. Firstly, using an image capturing unit to capture at least one captured image of an object to an image processing unit for calculation. The image processing unit can take linear brightness change of single captured image to estimate the image conversion parameters, also can take comparison of linear and non-linear images to estimate the image conversion parameters, further can take the difference of exposure quantities to estimate the image conversion parameters. Therefore, the estimation of the image conversion parameters can be finished well and easily.

The invention discloses a scene light source estimation accuracy improving method based on a camera response function. The method comprises the steps of calculating out a camera transfer matrix between a training image and a test image at first; then utilizing the matrix to convert the training image and a true light source of the training image into an image and a true light source under a camera which correspond to the test image; extracting characteristics of the converted image; learning a regression matrix between the characteristics and the true light source; finally utilizing the regression matrix to finish light source estimation of the test image and achieving the light source estimation between different image libraries. According to the method, any parameter is avoided, the transfer matrix between the cameras and the regression matrix between the characteristics of the training image and the light source can be determined by being calculated out once and can be directly applied to estimating light sources of images shot by the cameras used by the different training images, and the light source estimation accuracy between the different cameras can be effectively improved.

The invention provides an RGB image spectral reflectance reconstruction method based on a fused convolutional neural network, and the method comprises the steps: firstly building an image database, and carrying out the batch image preprocessing; training a deep convolution model based on a convolutional neural network by using an image database, and establishing a reconstruction method based on spectral nonlinear mapping and spatial similarity fusion; and finally, acquiring a to-be-reconstructed RGB image, preprocessing the to-be-reconstructed RGB image, inputting the to-be-reconstructed RGB image into the convolutional model for convolutional neural network feature extraction, outputting reconstructed spectral reflectance, and feeding back a reconstruction result to a user to complete a spectral reflectance process. The invention has the beneficial effects that the influence of factors such as image types, image sizes, camera response functions and light source spectral power on the reconstruction result is avoided, the model training time is greatly prolonged, and the problem that the reconstruction precision is not high due to the influence of a linear method or external factors is effectively solved.

The invention discloses a high dynamic range image fusion method in a compressed sensing domain based on NSCT and PCNN. The high dynamic range image fusion method comprises the following steps: 1) carrying out normalization processing on an input multi-exposure image sequence; 2) performing radiation calibration on the normalized multi-exposure image sequence by using a camera response function; 3) performing NSCT conversion on the multi-exposure image sequence after radiation calibration to obtain low and high frequency coefficients of each image; 4) performing compression sampling on the high-frequency coefficient of each image; 5) calculating the spatial frequencies of the low-frequency coefficient and the high-frequency coefficient of each image to serve as excitation signals of the PCNN, and carrying out fusion through a maximum decision; 6) carrying out compressed sensing reconstruction on the fused high-frequency coefficient; and 7) carrying out NSCT inverse transformation on the fused low-frequency coefficient and high-frequency coefficient to obtain a target high-dynamic-range image, completing the fusion of the multi-exposure low-dynamic-range image sequence while carrying out compressed sensing reconstruction by utilizing the NSCT, the PCNN and a compressed sensing method, and finally generating a high-dynamic-range image containing rich details.

The invention is applicable to the technical field of data processing and provides a method for generating ultra-high dynamic range images. The method comprises the following steps: obtaining a groupof images to be processed with different exposure; according to the image to be processed, the camera response function is restored, and the image to be processed is combined into a high dynamic rangeirradiance map by convex combination; the high dynamic range irradiance image is processed based on the hue mapping algorithm, and the ultra-high dynamic range image is outputted. The obtained ultra-high dynamic range image is clear, and there is no situations such as bright spot focusing, not clear dark spot, dark spot focusing, not clear bright spot.

The invention discloses a method for radiometric calibration of a color camera by using a multi-spectral image, namely solving an inverse camera response function and a spectral sensitivity function of the color camera. The method comprises the following steps: The intrinsic linearity between pixels in a multi-spectral image is searched, and the inverse camera response function is estimated by restoring the intrinsic linearity from a color (RGB) image. The spectral sensitivity function and the inverse camera response function coupled in the RGB image are solved by alternating iteration of theexisting data. The results show that the intrinsic linearity is not only the intrinsic linearity between pixels but also the intrinsic linearity between pixels. The invention uses the scene spectrum information provided by the multi-spectrum image, reduces the error caused by the inaccurate use information of the traditional method, improves the radiometric calibration accuracy of the camera, andsimultaneously obtains two calibration information of the inverse camera response function and the spectrum sensitivity function.

The invention discloses an improved VINS method for a complex illumination environment, and the method comprises the steps: estimating a camera response function through an initialization process, enabling the process to carry out the modeling and calibration of a camera imaging process, disassembling a light flow process from the relation between a pose and gray scale into the relation between the pose and exposure parameters and the irradiance through the function, and carrying out the recognition of the exposure parameters and the irradiance. Therefore, the influence of the exposure parameters on pose estimation can be estimated in the process. According to the method, the change of camera parameters caused by complex illumination can be integrated into the overall VINS system pose estimation, so that the system adapts to the environment. And finally, the feasibility and effectiveness of the algorithm are proved by comparing the algorithm provided by the invention with the traditional VIINS algorithm through a real experiment, so that the realization of the improved SLAM system under the complex illumination environment based on the traditional VINS algorithm is completed.

Systems and methods for registration of image pairs, including camera response function normalization are disclosed and include selecting a pair of images from a set of images, each of the pair of images having associated metadata, determining a camera response function for each of the images in the pair of images using the associated metadata, normalizing each camera response function for across the set of images, and applying the normalized camera response function to the pair of images. A deformation map is generated in a multi-scale process using registration parameters, to deform one of the pair of images to align with another of the pair of images. The image pairs may be selected by identifying image pairs having an overlap that exceeds an overlap threshold, having a sequential proximity in an image series satisfying a proximity threshold, and/or having estimated image capture attitudes within an attitude threshold.