The invention provides a JPEG image decoding method suitable for a multi-core embedded type platform. The method includes the steps of firstly, segmenting a JPEG image into image blocks in different sizes according to the loading condition of each operation core and distributing the image blocks to the operation cores respectively; then, conducting decoding and zooming on the image blocks distributed to the operation cores in a parallel mode through decoding threads operated in different operation cores and obtaining a decoding result of each image block; finally, conducting splicing on the decoding results of the image blocks to obtain a decoded image. The invention further provides a JPEG image decoder suitable for the multi-core embedded type platform. Through the JPEG image decoding method and decoder suitable for the multi-core embedded type platform, system computing resources can be effectively used, and the image decoding speed can be obviously improved.

JPEG image decoding method and decoder suitable for multi-core embedded type platform

The invention belongs to the digital image processing field and is for eliminating mole stripe patterns in fabric images. The fabric image mole stripe elimination method based on low-rank sparse matrix decomposition is characterized in that, local self-similarity of fabric textures and energy concentration distribution characteristics of the mole stripe in a frequency domain are utilized, a low rank sparse matrix decomposition model is constructed, the fabric textures are separated from the mole stripe pattern, and thereby a problem of mole stripes in the fabric image can be solved. The fabric image mole stripe elimination method based on low-rank sparse matrix decomposition is mainly applied to digital image processing.

Fabric image mole stripe elimination method based on low-rank sparse matrix decomposition

The invention discloses an image denoising method. The method comprises steps: an average block set of a to-be-denoised image is acquired; a corresponding mean value is subtracted from each average block in the average block set to obtain a noise block set; a first noise image in the noise block set is acquired; noise modeling is carried out on the noise block set according to a generative adversarial network to obtain a generator capable of generating the same kind of noise as the to-be-denoised image; a second noise image is acquired according to the generator; according to a noise-free image, the first noise image and the second noise image, a training set is constructed; according to the training set and a discriminative learning method, an image denoising network model is trained; andthe to-be-denoised image is inputted to the image denoising network model to acquire an image after denoising. The invention also provides an image denoising device, equipment and a storage medium. the denoising effects on unknown real noise in real life can be enhanced and the denoising efficiency is improved.

Image denoising method, device, equipment and storage medium

The invention provides a motor bearing fault diagnosis method and device which can convert a one-dimensional vibration signal of a bearing into a two-dimensional gray-scale map by adopting a sample-image conversion method and reduce complexity of a structure of a two-dimensional convolutional neural network. The method comprises the steps of: acquiring a motor bearing fault data set, converting aone-dimensional bearing fault signal in the fault data set into a two-dimensional fault signal gray-scale map to obtain a training set image; according to the obtained training set image, training a fault diagnosis model by the two-dimensional convolutional neural network; acquiring a one-dimensional bearing vibration signal in the field, and converting the one-dimensional bearing vibration signalinto a two-dimensional field signal gray-scale map; and inputting the two-dimensional field signal gray-scale map into the trained fault diagnosis model, outputting each fault category affiliation weight by the trained fault diagnosis model, and according to each output fault category affiliation weight, determining a fault category of a current bearing. The motor bearing fault diagnosis method and device are applicable to the field of motor bearing fault diagnosis.

Motor bearing fault diagnosis method and device

The invention provides a color filter array and an image processing method. The color filter array comprises color filter units and a color-free filter unit, wherein the color filter units comprise at least three primary color filters which are distributed adjacently, and each color filter unit comprises a red color filter, a green color filter and a blue color filter; and the color-free filter unit comprises color-free color filters which are distributed on the periphery of the color filter units. Because the color-free color filters allow all colors of light to transmit, light rays can be directly irradiated on an image sensor by virtue of the color-free color filters so as to ensure that the light-sensing performance can be improved. In addition, when image processing is performed, a color channel of colored images obtained by virtue of imaging performed by the color filter units in the color filter array and a brightness channel of gray level images obtained by virtue of imaging performed by the color-free filter unit in the color filter array are fused to obtain a required image, and thus the light-sensing performance and resolution degree of an imaging system can be further improved.

Color filter array and image processing method

The invention relates to an automatic deblocking method based on sparse representation, comprising the following steps of: based on the concept of synergetic filtration, repeatedly taking a decoded image block by utilizing a redundancy dictionary obtaining by training; then removing a part of high-frequency information in a sparse solution domain of the image block by utilizing a zero masking technology; newly training the dictionary by utilizing an image obtained by first-time deblocking, and repeating the process until a satisfactory effect is obtained. The method utilizes the concept that multiple-time sampling is carried out in the same position of an air space for acquiring an average and specific information is removed on a dictionary domain through sparse representation. The method comprises the steps of (1) seeking blocking boundaries; (2) processing different boundaries by classification; (3) updating a dictionary D by interaction. The invention can automatically detect the boundaries with blocking effects and adopts different deblocking methods according to different boundary strengths. By utilizing the principle of sparse representation, pixel blocks are recovered by only taking limited coefficients so that false boundary information of the collected pixel block can be removed, and simultaneously the texture information of the original image block can be reserved as much as possible.

Automatic deblocking method based on sparse representation

The invention relates to a computer image processing and visual detecting genecology. In order to provide a method which can exactly estimate the noise function of different CCD (charge coupled device) images and further can not increase the computing complexity, the technical scheme adopted by the invention is as follows: a signal-correlated noise estimating method for an image sensor comprises the following steps of: searching smooth blocks of an image by adopting a high pass operator-based image structure analyzer; sampling the blocks according to the arrangement mode of CFA (colorful filter array), to obtain a set which corresponds to each gray value, and computing the mean value and variance of the set, to obtain a set of noise estimating sample points; according the smoothness degree of the set of the image block of each sample point, computing the reliability of the sample point; and finally in combination with the sample points, the reliability of the sample points and the base obtained by using PCA (principal component analysis) in advance, rebuilding a noise horizontal curve by a sparse representation technology. The method is mainly used for processing the image.

Signal-correlated noise estimating method for image sensor

The invention belongs to the field of computer vision, and provides good detail while improving image contrast, and the natural performance of the tone is kept while the noise is suppressed. An underwater image enhancement method based on structure-texture layering comprises the steps of firstly, carrying out color correction through histogram equalization, decomposing the image subjected to colorcorrection into a low-frequency structural layer and a high-frequency texture layer, leaving the noise on the texture layer, accurately estimating the transmission rate from the structural layer without noise based on a proposed fog line model to conduct enhancement processing, enhancing the texture layer by using a gradient residue minimization method, reconstructing the reinforced structural layer, the texture layer and the refined edge mask to form a final enhanced image through scaling, so that the problems that can not be solved through existing technology is solved. The method is mainlyapplied to underwater image enhancement and processing occasions.

Underwater image enhancement method based on structure-texture layering

The invention relates to a compressed sensing based CCD (Charge Coupled Device) noise estimation method, which comprises the following steps of: 1) a single image CCD noise sample point estimation stage, ie accurately estimating the noise level of part of pixels in a single image and denoising by block based three-dimensional DCT (Discrete Cosine Transformation), and comprises the following substeps: searching proper image blocks, denoising the image blocks, and estimating the noise level of a sample point; and 2) a compressed sensing based noise level curve reconstruction stage According to the invention, only the noise level of part of a smooth area is accurately estimated, and the noise level function of the whole image is computed by utilizing the compressed sensing technology, nonuniform noise which is produced by a CCD and strongly depends on signal intensity can be more accurately estimated from the single image, and then a noise level curve is obtained By the invention, the accuracy of a noise level function is improved, simultaneously, the complexity of the computing is greatly reduced, and the obtained noise level curve can be applied in the image denoising, movement estimation, edge detection and other computer vision problems

Compressed sensing based CCD (Charge Coupled Device) noise estimation method

The invention belongs to the field of computer vision and provides a method for recovering a super-resolution depth map The technical scheme is characterized in that the depth map super-resolution method based on the double transform domains comprises the following steps: 1)establishing an optimization model; 2) designing a transform domain regularization term ESCD(x,z); 3) designing a spatial domain regularization term EMTV(x,z); and 4) solving the optimization model through an alternative direction method ALM, and alternatively updating the depth map and sparse coefficients, wherein when updating the depth map, utilizing an iteratively reweighted least squares algorithm IRLS, and when updating the sparse coefficients, utilizing a soft operator The method is mainly applied to the occasion of computer vision image processing

Yang Jingyu

Papers

Automatic deblocking method based on sparse representation

Signal-correlated noise estimating method for image sensor

Underwater image enhancement method based on structure-texture layering

Compressed sensing based CCD (Charge Coupled Device) noise estimation method

A depth map super-resolution method based on double transform domains