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 provides a method for non-local average value denoising of an image The method comprises the steps that the image to be denoised is obtained, an image block of a preset size is extracted, a vector matrix is formed by read spatial domain data of three color components of RGB of the image block, principal component analysis and dimensionality reduction are conducted, and therefore a residual vector matrix is generated after dimensionality reduction is conducted; in terms of the image block, according to preset S-level searching zones, a searching window of the preset size is moved from top to bottom and from left to right in the corresponding searching zone with a central pixel point of the image block serving as a center according to the search step size of each level of preset searching zone, and therefore a searching block can be obtained; the search step sizes of the searching zones are progressively increased in sequence; the Euclidean distance between the residue vector matrix corresponding to the searching block and a vector corresponding to each pixel point is calculated, the weight value of the image block is then calculated according to the Euclidean distance, a filtering value of the center pixel point of the image block is finally obtained according to the weight value, and therefore denoising of the image is achieved The invention further provides a corresponding system, the time complexity is low, and the denoising speed is high

Method and system for non-local average value denoising of image

The invention provides a video noise estimation method based on main component analysis. Video sequence correlation is fully used, and similar block searching is carried out between adjacent frames of images, and based on a minimum cost criterion, a differential image between the adjacent frames of images is acquired, and influences of video motion are eliminated, and then a preliminary weak texture difference image is acquired. Noise estimation based on video blocks is introduced, and noise level parameters are acquired in a self-adapting way, and a normal distribution function is adopted as a threshold function of selection of a weak texture block, and then calculation complexity is reduced. The estimated noise level is more accurate by adopting a clear iteration index, and then an underestimation phenomenon under a condition of a high noise level is prevented. The video noise estimation method can be used in a blind video denoising filed, and has advantages of accurate estimation and wide application prospect.

Video noise estimation method based on main component analysis

The invention discloses a method for quantitative evaluation of the curve/curved surface quality. To be specific, the method comprises: acquiring an original control point coordinate matrix of a curve/curved surface and carrying out translation transformation to obtain a new control point coordinate matrix; carrying out wavelet fairing multiple times on the new control point coordinate matrix to obtain a low-resolution curve/curved surface control point matrix, and carrying out wavelet fairing once to obtain a reconstruction matrix; with the reconstruction matrix, reconstructing the low-resolution curve/curved surface control point matrix till realizing the consistence with the original control point number of the curve/curved surface, and calculating a fairing information sum matrix of the curve/curved surface; calculating a detail information sum matrix of the curve/curved surface; and on the basis of the detail information sum matrix and the fairing information sum matrix, calculating a detail ratio and carrying out quantitative evaluation on the curve/curved surface quality based on details. The disclosed method has the clear steps; the evaluation accuracy is improved effectively based on specific values; and the computing process is simple and efficient.

Method for quantitative evaluation of curve/curved surface quality

The invention belongs to the field of computer image processing and aims to provide a framework based on deep learning to effectively estimate an image noise level. For this purpose, a technical scheme is adopted. According to the technical scheme, an image noise level estimation method based on deep learning comprises the steps that 1, a signal dependent noise (SDN) model is proposed; 2, data preprocessing is performed; 3, a neural network structure is constructed; 4, the learning rate and momentum parameters of a network are set, a deep learning framework caffe is utilized to train the convolutional neural network till cost loss is reduced to a certain degree and training reaches the maximum number of iterations, a training model is generated, and a cost loss function is selected; and 5,an image with noise is input into the trained model, and a noise level function is output. The method is mainly applied to computer image processing.

Image noise level estimation method based on deep learning

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.

Zhaoyang Wu

Papers

Signal-correlated noise estimating method for image sensor