60results about How to "Suppress image noise" patented technology

A platform Smart Scalpel system using rapid real-time feedback for effecting laser treatment. The platform system includes an imaging system for rapid real-time detection of tissue characteristics, a processing system for processing the detected characteristics, and a treatment system for effecting treatment in accordance with results of the processing. The platform system provides for preprogramming and real-time inputting conditions and parameters for diagnosis using the imaging system and/or treatment using the treatment system.

The invention puts forward a compressed sensing reconstruction method based on image nonlocal similarity. According to the method, image nonlocal similarity, a low-rank matrix and minimum total variation (TV) are combined, and two priors, namely, the local similarity and local smoothness of images, are fully utilized. On one hand, the block effect and the loss of global structure information caused by independent processing of single image blocks in the traditional method are eliminated, and on the other hand, real details of images are retained and false details produced by unreliable information are reduced or removed while noise is suppressed. High-quality compressed sensing reconstruction of images is realized. Compared with a general reconstruction method based on transform domain sparse or TV constraints, the method of the invention is of robustness to noise, better reconstruction quality is achieved, and great improvement is achieved both in visual effect and evaluation index.

The invention relates to a system and a method for measuring vision of a variable optical axis. The measuring system comprises a substrate, a camera, a rotary prism device and a computer, wherein the computer is connected with the camera and the rotary prism device respectively; the camera is arranged on the substrate through a camera adjusting mechanism; the rotary prism device is fixed on the substrate and aligns to the camera in the axial direction; an imaging plane of the camera and a prism plane of the rotary prism device are parallel to each other; and the rotary prism device comprises a prism capable of rotating in a complete cycle. Compared with the prior art, high-resolution target images can be collected by the camera under various optical axis pointing states respectively by sufficiently utilizing accurate pointing characteristic and wide-field scanning capacity of the rotary prism, the imaging view field and measuring range of the system can be enlarged obviously, relatively high imaging resolution and three-dimensional measurement accuracy are realized, and simultaneously, the integration and economical efficiency of system structure design also can be guaranteed.

The invention discloses an automatic image enhancement system and method for integrating multi-scale information. The system comprises a sample calibration module which collects a training set image,marks a pixel level of the training set image, determines a mapping range of a label, and obtains a corresponding reference standard image, a network automatic construction module which inputs a corresponding reference standard image, specifies an image range to be perceived, automatically calculates the size and number of convolution kernels in a multi-scale fusion module, and generates a convolutional neural network model, an automatic training network module which carries out offline training of the convolutional neural network model by using a random gradient descent method optimized pixelweight loss function, and a network application network which inputs a new image into the trained network and outputs an enhanced image with the size of an original image. By integrating multi-scalefeatures, context information is effectively utilized, the image is automatically enhanced, and manual intervention is not needed.

The invention discloses a haze removing method for a single image based on image haze concentration estimate. The method comprises the following steps: 1) inputting a haze image; 2) converting the inputted image from the RGB space into the HSV space; dividing SV channel images and calculating the characteristic vectors of the local blocks; 3) with the target of making the lowest image haze concentration, calculating the initialized optimal atmosphere transmission value for the haze image; 4) using a guide filter to refine the optimal atmosphere transmission value; and 5) calculating the refined optimal atmosphere transmission value, the atmosphere light estimation value as well as the brightness value of each pixel in the haze image to obtain a clear image. According to the invention, through the conversion of image haze removal into the problem of haze concentration minimizing, the atmosphere transmission value can be determined accurately so as to remove the haze as much as possible and to avoid the severe halo formation and image noise in the saturated areas. Not only the haze can be eliminated effectively, but also the halo formation and image noise can be controlled not to rise. Therefore, the method can be applied to different complex weathers.

The invention provides an image generation method and device, and terminal equipment. An image sensor that is provided in advance includes a photosensitive pixel array and an optical filter arranged on the photosensitive pixel array, the optical filter includes a filtering unit array, each filtering unit covers m*n photosensitive pixels which form a merged pixel, m*n>1, brightness information of a photographing environment is obtained, a unit pixel quantity P corresponding to the brightness information is determined according to a preset strategy, 1<=P<=m*n, and outputs of a plurality of photosensitive pixels which belong to a same merged pixel is correspondingly processed according to the unit pixel quantity, thereby generating an image of corresponding resolution. Adjustment of the light inlet quantity of a unit pixel is realized through dynamic adjustment of the unit pixel quantity according to the brightness condition of the photographing environment, and the image of corresponding resolution is generated, thereby suppressing noisy points of the image, and improving definition of the image.

A solid-state imaging element includes: a pixel section having a plurality of pixels arranged in a matrix form, each of the pixels converting an optical signal into an electric signal and storing the electric signal according to exposure time; a dummy pixel section having dummy pixels arranged in a matrix form; and pixel drive sections adapted to control the pixel operations in such a manner as to operate an electronic shutter on and read the pixel section and dummy pixel section, wherein when an electronic rolling shutter is operated in which the pixels are shuttered row by row, the pixel drive sections judge whether the current and next frames are shuttered concurrently and in parallel so as to determine in which horizontal read period the dummy pixel section is to be shuttered.

The invention provides an image enhancement method for scene self-adaptive wide dynamic infrared thermal imaging, which belongs to the technical field of image processing. The method comprises the following steps of calculating to obtain an initial low-frequency base layer image by using a bilateral filtering algorithm according to a 16-bit original image; calculating to obtain an initial high-frequency detail image; performing adaptive histogram equalization (CLAHE) operation on the initial low-frequency base image to obtain a first 8-bit low-frequency base image; performing global histogramequalization processing on the initial low-frequency base image to obtain a second 8-bit low-frequency base image; obtaining a final high-frequency detail image by utilizing automatic gain control operation according to the initial high-frequency detail image; utilizing linear weighting calculation to obtain a final low-frequency base layer image; and fusing the final high-frequency detail image and the final low-frequency base image to obtain an enhanced output image. According to the method, problems of poor scene adaptability and over-enhancement of the existing infrared thermal imaging wide dynamic range image enhancement technology are solved.

The invention discloses a non-local mean filtering method based on direction field estimation. The non-local mean filtering method comprises the following steps of receiving a discrete noise fingerprint image, establishing a direction field estimation model of a pixel block in the discrete noise fingerprint image, and performing non-local mean filtering on the discrete noise fingerprint image based on the direction field estimation model to obtain a final denoising fingerprint image. According to the non-local mean filtering method, the problem of poor inhibition performance and poor robustness on noise in the conventional method is solved, contrast among grains in the fingerprint image is enhanced, and characteristic information in the fingerprint image is protected from damage.

The invention belongs to the technical field of image processing, and more particularly relates to a GRHP based unmanned plane infrared image detail enhancement method. The method comprises the steps of sequentially performing denoising, filtering, subtraction, enhancing and synthesizing on an original image; performing adaptive threshold value histogram projection on synthesized image; and obtaining an output image. The method has the beneficial effects that the running efficiency is high, and an infrared video can be processed in real time when the unmanned plane has low hardware configuration; due to introduction of Gaussian constraint filtering, manual work effect is prohibited effectively, and the detail enhancement effect of the method on the infrared image is ensured; due to introduction of an adaptive detail enhancement coefficient, transient state continuity of the method in video processing is solved, and adaptation of the method to a scene is improved; and due to introduction of the adaptive threshold value histogram projection, image noise is further prohibited, furthermore, image contrast is effectively improved, and the detail enhancement capability of the method is improved.

An image forming apparatus is included of a pivotally rotated photosensitive drum, a developing roller for developing an electrostatic latent image formed on this photosensitive drum, and an intermediate transfer belt for temporarily holding the toner image which has been developed to be formed by the developing roller. This developing roller is equipped with a tracking roller which abuts against the photosensitive drum so as to keep a distance between the developing roller and the photosensitive drum as a constant distance. A direction of weight “a” produced by this tracking roller with respect to the photosensitive drum may be positioned within a so-called “wrap range” corresponding to an abutting range between the intermediate transfer belt and the photosensitive drum.

A box for an image reading apparatus, the box comprising a box body and a reinforcing member, the box body comprising a rectangular frame and a base plate fixed to the rectangular frame, and the reinforcing member being attached to the box body. The reinforcing member has a downwardly drawn area, upwardly drawn areas which are adjacent to the downwardly drawn area on front side and rear side for placing a reading block without interfering with the movement of an original document scanning unit and a cutout portion for disposing at least an image processing unit formed from the downwardly drawn area to end-of-scan side in the subscanning direction of the box body. An image reading apparatus having reduced thickness is provided by employing this box. An image forming apparatus in which height position of the image reading apparatus is set to such a height position that allows easy operation is provided.

The invention discloses a multi-tensor-based magnetic resonance diffusion weighted image structure adaptive smoothing method, relates to a magnetic resonance diffusion weighted image smoothing method, belongs to the field of medical image processing, and solves the problem that the accuracy of the fiber structure information of each obtained voxel is low because of poor noise suppression in the conventional method. The multi-tensor-based magnetic resonance diffusion weighted image structure adaptive smoothing method comprises the following steps: firstly, selecting related parameters, and setting an initial neighborhood radius; secondly, calculating the initial fiber structure information of each voxel; thirdly, calculating the weights of all voxels in the neighborhood radius on the voxel according to the fiber structure information, performing weighted smoothing on a magnetic resonance diffusion weighted image, and recalculating the fiber structure information of each voxel after the magnetic resonance diffusion weighted image is smoothed; fourthly, judging whether a stopping criterion for iteration is met or not, if not, expanding the neighborhood radius and continuing to performing the third step, or else, ending the calculating. The multi-tensor-based magnetic resonance diffusion weighted image structure adaptive smoothing method is applicable to processing the information of the magnetic resonance diffusion weighted image.

The invention provides an image denoising method and system based on edge tracking. The method comprises the following steps: S1, obtaining multi-frame continuous images; S2, selecting one reference image from the continuous images, and performing edge detection processing on the reference image to obtain a plurality of edges; S3, for each obtained edge of the reference image, searching for a corresponding edge in each non-reference image; S4, expanding each edge of the reference image to form an edge area, and for each pixel point in the edge area, replacing an original gray scale value by use of a median of the gray scale values of all the corresponding pixel points in the multi-frame continuous images; and S5, carrying out denoising processing on a non-edge area of the reference image. According to the image denoising method and system based on the edge tracking, low-noise edges are synthesized by use of edge information of adjacent frame data while image noise is inhibited; and high-frequency detail information of the edges and the like is effectively reserved.

The invention discloses an image enhancement method based on a black-white-color binocular camera. The problem of multispectral image enhancement is converted into the problem of low-rank matrix filling. Aiming to solve the problem that accurate stereo matching can cause high time complexity and is difficult to apply, an image enhancement method based on a black-and-white-color camera is provided,black-and-white image feature points and a color image are matched, color values of matching points in the color image are regarded as color values of the black-and-white image feature points, and the color of the black-and-white image is recovered through pixel points with known color values by means of a low-rank matrix filling method. According to the method, the operation efficiency can be effectively improved, the image sharpness is improved, the image details are enhanced, the image noise is inhibited, and the method has very important significance in the field of image processing.

The invention discloses an image sensor, a camera module and electronic equipment, and relates to the technical field of image processing. The image sensor comprises a pixel array and a plurality of amplifiers, the pixel array comprises a plurality of pixel groups arranged in the row direction and/or the column direction, each pixel group comprises a plurality of pixels with different colors, andthe amplifiers are in one-to-one correspondence with the pixels; the input end of the target amplifier is connected with the output end of a target pixel, the output end of the target amplifier is connected with a column line where the target pixel is located, the target amplifier is any one of the amplifiers, and the target pixel is a pixel corresponding to the target amplifier.