182 results about "Adaptive histogram equalization" patented technology

Methods for contrast enhancement of digital images are provided. A method of adaptive histogram equalization is provided that determines weighting factors for discriminating between sub-regions of a digital image to be more enhanced or less enhanced. Another method for content adaptive local histogram equalization is provided that uses a mapping function in which the dynamic range is not changed by the transformation. A third method for contrast enhancement is provided that includes dividing a digital image into a plurality of regions of pixels, and for each region in the plurality of regions, determining a threshold gray level for the region, generating a mapping curve for the region based on the threshold gray level, and applying the generated mapping curve to each pixel in the region to enhance contrast.

Dynamic range equalization by histogram modification. A histogram is analyzed to determine locations of peaks. A mapping function is formed which relates to the locations of the peaks in a histogram. That mapping function may have areas of highest slope near the peaks. The mapping function is used to form a compressed histogram, which has the required number of levels to display on a display device.

The invention relates to a low-quality image enhancement method under extreme weather conditions. According to the method, as for an inputted single image, the image is converted to a CIE-Lab color space; a color cast factor D is set; according to experience, if D is smaller than 1.4, the image is a clear image which does not require processing; if D is larger than 1.4, the image is a degraded image; whether the image is a dust image or a haze, rain and snow image is judged according to chrominance component values; if the image is in a haze, rain and snow image, an improved dark primary color priori algorithm is adopted to process the image; if the image is a dust image, a gamma-corrected contrast limited adaptive histogram equalization algorithm is adopted. With the method adopted, problems in the prior art can be well solved. The effects of the method are greatly improved compared with the in the prior art, and the popularization and application of the method can be benefitted.

The invention relates to a digital image reinforcing method based on wavelet reconstruction and decomposition and a device thereof, which belongs to the image data processing field. The digital image reinforcing method comprises image pretreatment, decomposition process, transformation process and reconstruction process, and is characterized in that the image decomposition process comprises multi-group integrals and down-sampling process; the transformation process comprises the statistics, analysis and modification processes of mid data information; the reconstruction process comprises multi-group up-sampling, integrals and adding processes; in the transformation process, adaptive histogram equalization technology and noise restraining are adopted. The invention, based on multi-scale analysis, realizes to reinforce the contrast of digital image and prevents noise simultaneously through the wavelet decomposition, transformation and reconstruction and adopting the adaptive histogram equalization technology and noise restraining in transform domain and can be widely used in such fields related to the improvement of digital image quality as medical information systems, industrial nondestructive testing systems and other similar image systems.

The invention provides a method for accurately reconstructing a microcosmic finite element grid model of a dissimilar material on the basis of CT (computed tomography) images. According to the method, sequence CT images are acquired through industrial CT, and micro-structural information in the CT images is mapped onto the reconstructed finite element grid model on basis of digitization and threshold segmentation, so that any detailed structural information in the dissimilar material can be represented in the reconstructed model. The method improves the reconstruction accuracy by means of contrast-limited adaptive histogram equalization, median filtering and pixel interpolation, and improves the reconstruction efficiency through image cut and pixel combination. With the method, rectangular (two-dimensional) and cuboid (three-dimensional) unit grid models with higher finite element analysis accuracy are directly reconstructed, error accumulation during reconstruction, grid partition and other links of the existing geometric reconstruction method is avoided, and reconstruction accuracy and efficiency are improved. The method can be widely applied to fields such as performance prediction and optimization design of dissimilar materials.

The invention relates to an automatic eye fundus image vessel detecting method based on a PCNN (pulse coupled neural network). CLAHE (contrast limited adaptive histogram equalization) with limited contrast ratio and two-dimensional Gaussian matched filtering are used for processing an eye fundus image, pixel EOL (energy of Laplace) is used, on the basis of simplified PCNN models, as link intensity value of corresponding PCNN neurons, the eye fundus image is then cut according to the maximum interclass variance standard, and final vessel detecting results are obtained through area filtering. By fully utilizing advantages of combination of the PCNN and the method, the vessel network of the eye fundus image can be fully and accurately detected.

The invention discloses a method for extracting roads from a remote sensing image, which belongs to the technical field of image processing and solves the problem that the existing technology is not precise in detection and positioning of roads, and has a large number of false targets and bad continuity. The specific realization process comprises the following steps of: firstly implementing pretreatments including adaptive histogram equalization and Frost de-noising on the input images; then implementing three layers of non-sub-sampled contourlet transform thereon, decomposing each layer into eight directions, extracting the model maximum value of each direction sub-band of the first layer and the second layer as the linear characteristic vectors of roads; clustering the obtained characteristic vectors by using fuzzy C means clustering algorithm to obtain the initial extraction results of roads; and finally implementing non maximum value inhibition and road post treatment based on the spatial relationship to the initial extraction to obtain the final road extraction result. The invention has the advantages of accurate road positioning, good integrality, low calculation complexity and no need of training and learning, and is used for analysis and processing of the remote sensing image.

The invention relates to a contrast enhancement method based on wavelet transform image decomposition and belongs to the field of image processing and computer vision detection. The method firstly separates high and low frequency information of an image with nonuniform light bar brightness by wavelet transform, and then removes the noise of high-frequency information by a wavelet transform multi-scale threshold denoising method, enhances the contrast of low-frequency information by a contrast restriction adaptive histogram equalization method, and finally, obtains an enhanced image by inverse wavelet transform. The method processes high and low frequency information respectively by using wavelet transform and the contrast restriction adaptive histogram equalization method, removes the noise to a great extent, enhances signal intensity and effectively improves the uniform uniformity of the light bars.

The present invention relates to an automatic exposure method based on adaptive threshold segmentation and histogram equalization. In accordance with a principle of image brightness being proportional to exposure time, when the photographed image is underexposed, a reflection on the image is that a mean value of the brightness is smaller, and the image after being processed by histogram equalization, will cover grayscale level values of the entire grayscale scope, that is, a grayscale mean value of the image after histogram equalization is larger. The grayscale mean value is adjusted according to tnew=told*(Yt/m(Y)), and the calculated tnew is longer than told, therefore, an aim for increasing exposure time upon underexposure is achieved, and vice versa. With the automatic exposure method based on adaptive threshold segmentation and histogram equalization, technical problems in the prior art of underexposure or overexposure of photographed subjects and target image adaptive segmentation are solved, contrast of the image is enhanced, and adaptive segmentation of a target image as well as effective exposure control of a segmented target image can be realized under any temperature circumstances.

A method for enhancing a radiographic image. The method includes obtaining image data for the radiographic image, generating conditioned image data by increasing differences between neighboring portions of at least a relatively uniform area of the radiographic image, generating an enhanced image by applying contrast limited adaptive histogram equalization to the conditioned image data, applying interpolation to the enhanced image, and displaying, storing, or transmitting the enhanced image.

The invention discloses an image processing method and device. The method includes the steps that fog conduction judgment is conducted on an image to be detected, when it is judged that the image is collected in a heavy fog day, and the image to be detected is divided into a plurality of sub image blocks. For each sub image block, histogram clipping is conducted through a contrast limited adaptive histogram equalization (CHAHE) algorithm, and the clipping threshold value of each sub image block is obtained through a formula (please see the formula in the specification), wherein the Tc, x represents the chipping threshold value of the sub image blocks omega (x) with the x as the center, and the size being M*N, the Tc, max is the maximum chipping threshold value, and the is the average value of the atmospheric transmission rates corresponding to all pixel points in the sub image blocks omega (x). According to the image processing method and device, the situation that the atmospheric transmission rates of all the pixel points in different sub image blocks are different is considered, and different chipping threshold values are set for different sub images, so that the ideal defogging effect of all the sub image blocks is achieved, and the good defogging effect of the finally combined image is achieved.

The invention relates to a SIFT and Otsu matching based colored eyeground image splicing method. The method is realized by that (1) a green channel of a reference eyeground image and a target eyeground image is extracted; (2) the green channel is preprocessed via contract limited adaptive histogram equalization and two-dimensional Gaussian filtering to enhance the contract between vessels and tissue background; (3) SIFT characteristics of the image are extracted; (4) characteristic points of the two images are matched initially via a nearest neighbor and a next nearest neighbor algorithm; (5)an Otsu algorithm is used to screen the initial matching points to obtain correct matching characteristic point pairs; (6) a random sampling consistency algorithm is used to calculate an affine transformation matrix to carry out space transformation o the images and complete registration; and (7) the registering images are fused in a maximum value algorithm, and splicing is completed. According tothe invention, SIFT is combined with the Otsu method, the disadvantage of non-ideal registering effect caused by mismatching points is overcome, and the method has significance in the field of diagnosing diabetes and pathology degree thereof.

The invention relates to a method for detecting a ship trail of an ocean synthetic aperture radar image. The method comprises the following steps: firstly, identifying and removing an extraordinary area from the image; performing histogram equalization treatment; and lastly, performing Radon conversion and converting the image so as to convert a linear structure in the image into a peak/valley point of a conversion domain, thereby detecting and obtaining the features of the linear structure in the image. According to the method provided by the invention, the ultra-bright or ultra-dark extraordinary area is automatically identified and is removed when a valid data template is generated, and then the problem of uneven brightness of the whole image is further solved by adopting the self-adaptive histogram equalization, and lastly, the normalizing Radon conversion is utilized to convert the weak linear ship trail in the complex ocean SAR (Synthetic Aperture Radar) image into the obvious peak/valley point in a transformation space, so that the effectiveness of the detection for the ship trail is greatly increased.

A video processor includes a video stream translation module configured to generate a translated luminance value for a pixel of a current frame of a video data stream. The translated luminance value is based on a first luminance value for the pixel and a first translation matrix for the current frame of the video data stream. The video processor includes a filter configured to generate an output luminance value for the pixel based on the translated luminance value and a target translated luminance value for the pixel. The output luminance value may be based on a weighted average of the translated luminance value and the target translated luminance value using a first weighting factor. The video processor may include a first weighting factor generator configured to generate the first weighting factor based on luminance values of the current frame of the video stream.

Methods and systems of processing an image. One embodiment includes applying a controlled, adaptive histogram equalization technique to improve the quality of an image. Another embodiment may include classifying an image. Both embodiments may use a concentration ratio of an image or portions thereof.

The invention discloses an image enhancement method based on space domain and belongs to the technical field of image enhancement methods. Based on index for measuring image quality, advantages and disadvantages of the image enhancement method are evaluated. The method comprises steps of 1) obtaining an original image; 2) performing grey level transformation on the original image so as to obtain a grey level image; 3) performing histogram equalization on the grey level image; and 4) performing histogram specification enhancement processing on the grey level image processed via the histogram equalization. The method is used for image processing.

The invention provides an infrared and visible light image fusion method combining image saliency and non-downsampled contour wave transform. Firstly, the contrast of visible light image is calculated. If the contrast of visible light image is low, the adaptive histogram equalization with limited contrast is carried out. The improved Frequency Tuned (FT) algorithm is used to extract the saliency region of infrared image, and then the background of the saliency image is suppressed. Then, non-down-sampled contour transform (NSCT) is performed on the infrared image and the processed visible imageto obtain low-frequency and high-frequency information, respectively. In the part of low frequency information, the saliency map is used for fusion, and in the part of high frequency information, thefusion rule of taking the large absolute value is used for fusion. At last, the fusion image is obtained by inverse NSCT with the low frequency coefficient and high frequency coefficient. The invention makes the fused image more abundant in detail information, more prominent in the target area, better in the visual effect of the fused image, and more suitable for human eye observation and computer processing.

The invention relates to the field of multimedia information security, in particular relates to a reversible information hiding method based on blocked self-adaptive histogram translation. The method comprises the following steps: (1) blocking a carrier image, and computing a peak value of each sub-block histogram; (2) self-adaptively selecting a sub-block for embedding information according to embedding capacity of each sub-block; (3) adopting a method for self-adaptively selecting histogram translation direction while embedding the information. The invention is a novel histogram translation method which is capable of effectively embedding the information and losslessly extracting the embedded information and recovering an original image; through the adoption of the blocked method, the embedded information is safer and the integral embedding rate is improved, the self-adaptive selected histogram translation direction can effectively improve the peak signal to noise ratio of the image to further improve the image quality, thereby providing help for the medical treatment, military and the like applications. Through the adoption of the method, the higher the precision of the image is, the better the reversible information hiding effect is.

The invention relates to an automatic exposure method based on histogram equalization, which includes steps of acquiring a frame image, recording exposure time of a frame, computing average value of the brightness of the acquired frame image, performing histogram equalization on the acquired frame image, computing average value of gray level of the image after the histogram equalization, computing exposure time of the next frame according to the principle that the exposure time is in direct proportion to the average value of the brightness of the image, setting exposure adjustment tolerance and the like. By the aid of the automatic exposure method, the technical problems of insufficient exposure or overexposure of a pickup body in the prior art are solved, the average value of the gray level of the image after histogram equalization is used as the optimum average value which can reflect characteristics of the brightness of the image sufficiently and is dynamic and can be suitable for environments with various brightness.

The invention discloses a road information remote sensing extraction method based on a pulse coupling neural network method. The extraction method comprises the following steps: the step 1): preprocessing an original remote sensing image, that is, utilizing the principal component analysis method to acquire the image of the first component of the original high resolution image, and using a self-adaptation histogram equalization method to perform image enhancement processing; the step 2): utilizing a pulse coupling neural network method to perform segmentation processing of the enhanced image; the step) 3: acquiring a normalized differential vegetation index diagram from the original image, and using the segmented image to subtract the normalized differential vegetation index diagram to eliminate the influence of the vegetation on the segmented image; and the step 4): using a mathematical morphology method or other methods to perform postprocessing to acquire the final road information.

The invention discloses a nighttime color image enhancement method based on purpose optimization and histogram equalization, and belongs to the technical field of image processing. The nighttime color image enhancement method based on purpose optimization and histogram equalization is characterized by comprising the steps that optimum control is carried out on the quadratic sum of the differences of pixel values of points of an image to be processed, gradient components, in the x direction and y direction, of the points of the image to be processed, the quadratic sum of the differences of pixel values of points of a result image, and gradient components, in the x direction and y direction, of the points of the result image, so that image contrast is improved, wherein a parameter theta which controls the gradient components is used for controlling the gradient components, in the x direction and the y direction, of the result image, when the absolute value of the gradient component, in the x direction, of each pixel point of the image to be processed and the absolute value of the gradient component, in the y direction, of the pixel point of the image to be processed are smaller than the theta, the values of the gradient components, in the x direction and the y direction, of the corresponding point of the result image are kept unchanged, and when the absolute value of the gradient component, in the x direction, of each pixel point of the image to be processed and the absolute value of the gradient component, in the y direction, of the pixel point of the image to be processed are larger than or equal to the theta, the values of the gradient components, in the x direction and the y direction, of the corresponding point of the result image are enhanced through a Gamma correction function; improved histogram equalization processing is carried out, and therefore a result which is better than an existing algorithm provided by Michael Elad and an MSRCR algorithm is obtained.

The invention relates to an image defogging method based on dark channel prior and adaptive histogram equalization. The method comprises the following steps: firstly, acquiring a fog image as an inputimage, and estimating an atmospheric light value in an atmospheric scattering model by adopting a quadtree algorithm; Secondly, estimating the simplified image transmissivity according to the atmospheric light value; Then, the estimation of the transmissivity is optimized by using a tolerance error correction mechanism; Substituting the optimized transmissivity estimation value into an atmospherescattering model to derive an initial defogging result of the image; And finally, enhancing the defogging result by using a contrast-limited adaptive histogram equalization algorithm (CLAHE) to obtain a final defogging result. The image defogging method based on dark channel prior solves the problems of color distortion and dark brightness of an image defogging algorithm based on dark channel prior, and the defogged image is richer in details and natural in color.

The invention discloses a low-illumination color eye fundus image judgment and enhancement method. The judgment method comprises the steps that an original color eye fundus image is inputted and converted to an LAB color space; the mean gray value muL and the gray mean square error sigmaL2 of the L channel component of the LAB color space are calculated; and the mean gray threshold mu and the gray mean square error threshold sigma2 of the L channel component are preset, and the image is judged to be a low-illumination eye fundus image if muL<mu and sigmaL2<sigma2. The enhancement method comprises the steps that normalization and gamma correction are performed on the three components of L, A and B of the LAB color space so that three components of Lgamma', Agamma' and Bgamma' are obtained; the Lgamma' component is processed according to a limit contrast self-adaptive histogram equalization method so that an LP' component is obtained; the Agamma' and Bgamma' components are processed so that AP' and BP' are obtained; reverse normalization is performed on LP', AP' and BP'; and the image is converted to an RGB color space from the LAB color space and the image is outputted. The overall brightness of the low-illumination color eye fundus image can be simply and accurately judged and effectively improved.

The invention discloses an eyeground image blood vessel segmentation method based on self-adaption difference of Gaussians. The segmentation method comprises steps of 1) extracting colorful eyeground image green channels, and performing pretreatment of self-adaption histogram equalization and anisotropy coupling diffusion with limited contrast ratio; 2) constructing Gaussian scale space; 3) subtracting adjacent two layers in the Gaussians scale space so as to get difference of Gaussian images; 4) averaging difference of Gaussian image weighing so as to get blood vessel increasing images; 5) performing binaryzation for the blood vessel increasing images; 6) rotating Gaussian kernel in 12 directions, wherein 15 DEG is regarded as step size in 0-180 DEG, repeating steps of 2-5, and overlapping results in the 12 directions; 7) selecting 20% of the grey value of the second peak as a light area of a threshold value extracting images according to bimodality of a pretreatment image histogram; and 8) reducing light areas from the blood vessel binary images so as to reduce effects of the light areas on segmentation of blood vessels. The method is widely applicable for blood vessel segmenetaion of all kinds of colorful eyeground images.