158results about How to "Good fog removal effect" patented technology

The invention discloses an automatic image defogging method based on dark primary color, which is used for solving the problem of information loss because the traditional defogging method highlights details by improving foggy day image contrast. The method provided by the invention comprises: A. calculating the dark primary color of the primary fog images and relevant atmosphere light value; B. according to the luminance component image of the original fog image, calculating a transmission image reflecting local fog concentration in an atmospheric scattering model; and C. determining the defogged primary image according to the fog image, the transmission image and the atmosphere light value in the atmospheric scattering model. Because of being built on the basis of a physical model, the invention can process various fog images in a self-adaption mode; and defogged images have favorable edge details and ideal contrast, and the clarifying effect is superior to the traditional defogging method based on image enhancement.

The invention discloses an image dehazing method and system based on a dark channel and a non-local prior, which belongs to the technical field of image information processing. The image dehazing method can reasonably recover a degraded image collected in the haze weather. The method comprises the following steps of (1) calculating a dark channel image of a hazed image; (2) estimating the atmospheric light intensity of the hazed image according to the dark channel image; (3) estimating the transmissivity of the image based on the non-local prior; (4) correcting a transmittance image; and (5) using an atmospheric scattering model to restore a dehazed image. The invention also discloses an image dehazing system based on the dark channel and the non-local prior. The invention can restore scene information in the image realistically and naturally, and the algorithm has the advantages of low complexity, fast running speed and wide application prospect.

The invention, which belongs to the technical field of image processing and computer vision, provides an image defogging method for eliminating a fog effect in an image effectively to realize image defogging based on depth learning and prior information. According to the image defogging method, an imaging model of foggy weather is expressed as follows: I(x)=J(x)t(x)+A(1-t(x)), wherein the I(x) expresses an image shot on a foggy day, the J(x) expresses a clear image, the A indicates global background light, and the t(x) belonging to a set of [0,1] indicates medium transmissivity; and the medium transmissivity is expressed as follows: t(x)=exp(-betad(x)), wherein the indicates an atmospheric attenuation factor and the d(x) expresses a scene depth. Estimation is carried out based on combination of a deep learning technology with foggy image prior information; and a clear image is restored based on an imaging model by using a reverse compensation technology. The image defogging method is mainly applied to an image processing occasion.

The invention relates to an image defogging method. To solve the problems that city streetscape/traffic monitoring images shot in foggy days are not clear, target objects such as vehicles and pedestrians in the images are not easy to recognize and the like, foggy-day images are integrally enhanced, and then high-frequency information of the images is extracted to strengthen details to finally acquire clear defogged images. The method includes collecting the foggy-day images, performing overall image enhancement on the foggy-day images by the Retinex algorithm, adjusting the details of the images and integrating the images. Tests prove that defogging effect of the Retinex<+> wavelet transform method is better than that of the Retinex algorithm and the Dark Channel algorithm. According to the image defogging method, auxiliary information such as depth information is not needed, and complex methods such as 3D (three-dimensional) modeling are not needed, so that defogging can be realized simply and quickly, and good real-time performance is realized.

The invention provides a single image defogging method based on a convolutional neural network. The method comprises the steps that firstly, a training set is constructed to serve as input of a deep convolutional neural network model, the network model comprises a shallow neural network model and a deep neural network model, and the shallow neural network model is used for extracting and fusing features of RGB color space of a foggy image and outputting a scene depth map of the foggy image; and the deep network model performs multi-scale mapping, pooling, convolution and other operations on the scene depth map on the basis of the shallow network model, and outputs a transmissivity map of the foggy image. And finally, recovering the fog-free image through the transmissivity, the atmosphericlight value and the atmospheric scattering model. According to the method, the characteristics of the RGB color space of the atomized image are extracted and fused to construct the shallow convolutional neural network model, and the shallow convolutional neural network model is connected with the multi-scale deep neural network model to establish the end-to-end neural network model, so that defogging clearness can be realized in various scenes, and particularly, color distortion of the image can be avoided in a dark environment.

The invention discloses an image defogging method facing to an atmospheric scattering proximity effect. The method comprises the following steps: 1) obtaining the image transmittivity of a primary image and obtaining an initial defogging image of the primary image through an image defogging algorithm; 2) establishing a radiation transmission equation of the image according to the image transmittivity of the primary image; 3) solving the radiation transmission equation to obtain an atmospheric point diffusion function; 4) performing the deconvolution processing on the initial defogging image according to the atmospheric point diffusion function to obtain a final defogging image. The method provided by the invention can be used for reducing the influence of the atmospheric scattering proximity effect on the imaging image, improving the contrast and definition of the image, increasing the image detail and achieving a good image defogging effect.

The invention discloses a digital video image enhancement achieving system and method based on an FPGA. The digital video image enhancement achieving method based on the FPGA mainly comprises the steps of dehazing processing on a current frame image and anti-reflection processing on the current frame image, wherein the step of dehazing processing on the video image is achieved through a dark channel prior dehazing algorithm, and a good dehazing effect can be achieved; the step of anti-reflection processing on the current frame image is achieved through an image histogram equalization algorithm, and the effect of converting a low-illumination-level gray image into a clear image can be achieved. By the adoption of the image dehazing process and the image anti-reflection process, the brightness, contrast ratio and color saturation of the image can be improved, and the image can be clearer, transparent and saturated in color.

The invention belongs to the field of video image enhancement, and particularly relates to a video image sea fog removal and clearing method which integrates frame difference method background estimation with the rapid single-frame sea fog removal algorithm based on edge detection and is used for an offshore aircraft rapid video image sea fog removal and clearing system. The video image sea fog removal and clearing method includes the steps of obtaining a sea fog video image, conducting sea fog removal and clearing on a single-frame sea fog image, and conducting sea fog removal and clearing on the video image. The video image sea fog removal and clearing method is suitable for all offshore aircrafts, and performance of visual systems of the offshore aircrafts in sea fog can be greatly improved. The operating speed is high, and sea fog removal and clearing can be conducted on the video image in real time in the sea surface scene. Compared with other algorithms, the rapid single-frame sea fog removal algorithm has a good edge keeping effect. The method has the advantages of being remarkable in fog removal effect and good in image restoration effect. The detecting performance, the tracking performance and the recognizing performance of targets in the later period can be effectively improved with sea fog removal and clearing as earlier stage processing on the visual systems.

The present invention relates to a dark channel prior based traffic video real-time defogging method. The method comprises three stages of: stage 1, down-sampling YUV data directly to form an RGB image, and calculating an atmospheric light value and the transmittance after down-sampling; stage 2, directly reconstructing YUV data of an original image into defogged YUV data by using the transmittance obtained from the previous stage; and stage 3, performing contrast stretching on the defogged YUV data in the previous stage. According to the method, a better defogging effect can be achieved, and real-time processing of a 1080P YUV video can be realized; and contrast stretching is performed on a processing result to further improve the visual effect, so that the method is of great significance in fields of safe driving of drivers, traffic monitoring systems and the like.

The invention discloses a method for improving traffic video image definition based on a dark channel technology. The method includes the steps that dark channel processing is carried out on traffic video images of a road in adverse weather (like haze and the like) to obtain dark channel images; a sky brightness value is obtained according to the dark channel images; an atomization concentration value is set, and a physical model of the atomized images is set up; a transmittivity distribution law function is improved, defogged images are obtained through processing, and the peak value signal-to-noise ratios are calculated; the peak value signal-to-noise ratios are compared, and the defogged traffic video image with the optimum effect is output. The transmittivity distribution law of the dark channel images is improved, a threshold value is set for the regions which do not meet dark channel priori conditions, and therefore the defogging effect is better; in addition, the quality of image processing is evaluated by introducing the peak value signal-to-noise ratio evaluation method, and therefore the images can be processed more intelligently.

The invention discloses an image defogging method based on a multi-scale dense connection network. According to the method, images with different degrees of fog are reconstructed as relatively clear images, so that the quality and vision feeling of the images can be greatly improved. The invention discloses a self-adaptive histogram equalization mode for image preprocessing to increase contrast ratios of images, and a defogging effect can be remarkably improved; characteristics of different degrees of fog can be described by using a multi-scale dense connection convolutional nerve net, in addition, with effective combination of the characteristics, a most effective defogging effect can be achieved; an equation based on Retinex defogging problems is provided, so that end-to-end deep learning defogging can be carried out relatively concisely and effectively; compared with other defogging algorithms based on deep learning, the method has the effects that not only is the number of model parameters greatly reduced, but also an ideal defogging effect can be achieved under a condition of a very small amount of training data.

The invention discloses a single image defogging method based on sky region segmentation processing, comprising the steps of: calculating the preliminary estimation transmittance of an input image according to a dark channel prior theory, optimizing the preliminary estimation transmittance respectively through median filtering and guide filtering, combining two results, and replacing the transmittance optimized by halo region guide filtering with the transmittance optimized by median filtering; employing a Canny boundary operator to calculate the boundary images of original images, correcting miscalculated regions, dividing the images into sky and ground regions to be used as binary template to extract the sky region transmittance after guide filtering optimization, and performing non-linear expansion processing on the extracted sky region transmittance; combining the transmittance respectively obtained through the two steps, and performing guide filtering smooth processing; and utilizing obtained optimization transmittance to perform filtering processing. The single image defogging method can better improve halo effects and sky region color distortion, and greatly improve defogging effects of fog-degraded images.

The invention discloses a single image defogging method, which mainly solves the problems of constrained nonlinear fitting ability, cumbersome training and single applicable scene in the prior art. The scheme comprises: constructing a convolutional neural network consisting of a feature sharing part, a haze atmospheric light value estimation branch and a transmittance estimation branch under the Caffe framework; acquiring a group of fog-free image set J, artificially fogging J to obtain a fog image set I; averagely dividing I and J into multiple paired image groups by batch size, and sequentially inputting in order for 200000 times to the neural network for training; inputting the image I that needs to be defogged into the trained neural network, and outputting the atmosphere light value Aand transmittance T; and calculating according to the atmosphere light value A and transmittance T to obtain a fog-free image J. The invention may well maintain the contrast and color saturation of the restored image, with the peak signal to noise ratio and the structural similarity both superior to the prior art, and may be used for the clear processing of the foggy image.

The invention relates to an image defogging system based on deep learning and an a priori constraint. The image defogging system comprises the steps of 1) performing visual observation comparison andmean square error statistics on image samples formed by a fog-free image and a synthetic atomized image, and proposing a new image defogging a priori constraint; 2) synthesizing an image sample set ofan HDF5 data format required for a training model by an atmospheric scattering model; 3) for the image defogging, taking the a priori constraint as the guidance, designing an end-to-end multi-scale deep convolutional network, and using the image sample set to optimize the training process of the model by combining with a multi-scale distance loss function; and 4) using the multi-scale deep convolutional network model obtained by the above training to realize the defogging operation of the real atomized image. The invention proposes a simple and effective a priori constraint, the visual contrast of the atomized image can be recovered through the multi-scale deep convolutional network model, the image texture is enhanced and the image defogging function is achieved.

The invention proposes a video image enhancement processing method based on background reuse. A conclusion that a common background has invariance property in a video recorded by a fixed camera is made through observation and comparison. The method carries out the background reuse according to the invariance property. The method comprises the steps: firstly recognizing a moving object in the video through employing an inter-frame difference algorithm, finding the minimum and maximum coordinates of the moving object through the continuous comparison with a threshold value, and marking the rectangular frame of the moving object; secondly carrying out the background extraction and refreshing through employing a simplified continuous frame difference method; thirdly respectively carrying out the defogging of the moving object and the background through employing a dark primary color prior method, carrying out the frame-by-frame defogging of the moving object, carrying out the timing processing of the background, combining the background with the moving object after defogging, and completing the defogging of the video. The method advantageous in that the method can greatly improve the operating speed in the video with the fixed background, and achieves the high-quality video defogging effect.

The invention relates to a road scene defogging method based on a conditional generative adversarial network. The road scene defogging method comprises the following steps: 1) constructing a trainingset and a test set by adopting a RESIDE data set; 2) taking the foggy picture x in the training set as the input of a generator G and generating G (x), namely a fogless picture; 3) sending the generated fogless picture to a discriminator D to judge whether the picture is true or false; 4) obtaining an optimal generation model after iteration setting for rounds, and 5) applying the obtained optimalgeneration model to a real foggy image for defogging. Compared with the prior art, the method has the advantages of good robustness, short processing time and the like.

The invention relates to a moving image processing device and a moving image processing method. The device and the method are based on an atmosphere model so that videos in foggy days are clear, the image visibility is improved, and in addition, the real-time processing requirements of the images can be preferably met. According to the moving image processing device, in the video defogging process, the video is divided into a core frame used as a main frame and an ordinary frame used as a sub frame, the core frame is calculated again to be used as a distance parameter t(X) and an unplugged point parameter A, the ordinary frame is not subjected to A calculation, and the A of the core frame is used, the t(X) of a region corresponding to the core frame is used for the background part of the ordinary frame, and the t(X) is calculated again for the foreground part of the ordinary frame. According to the device and the method, the application speed of the single-frame image defogging algorithm based on the atmosphere model in the defogging clarifying processing on the moving images such as videos and the like can be accelerated, a good defogging effect can be obtained, and in addition, the real-time performance of the moving images is ensured.

The invention discloses an image demisting method based on sky segmentation and transmissivity adaptive correction, an iterative threshold segmentation algorithm is used to divide the fogged image into sky region and non-sky region, and the brightness of the segmented sky region is adjusted. A dark channel prior algorithm based on fast bilateral filtering and cubic logarithmic polynomial correctedtransmittance is used to de-fogging the non-sky region. At the same time, the average value of high luminance pixel set in dark channel image is used to ensure that the estimation result of atmospheric light value tends to reasonable value as far as possible. The output image is further enhanced by fast bilateral filter and cubic logarithmic polynomial transform of transmittance, so as to achievethe purpose of denoising, preserving edge and eliminating the edge halo of the scene. The experiment results show that the invention has the advantages of good demisting effect, high color fidelity of the sky region and strong edge retention ability.

The invention discloses a single image defogging method based on a priori knowledge guide condition generative adversarial network. The method comprises the steps of 1, establishing an atomized image training set; 2, performing preliminary defogging on a single random foggy image; 3, performing defogging training on the preliminary defogged image; 4, calculating true and false values of the defogging training image of the reference true value image and the preliminary defogging image; 5, calculating an image loss objective function; 6, updating the weight parameter set; 7, calling a new single random foggy image, and repeating the step 2 to the step 6 until the true and false values reach a set value; and 8, defogging a single actual foggy image. According to the invention, priori knowledge is utilized to guide a coding network to generate a fog-free result. A part of useful information obtained through priori knowledge is utilized. Meanwhile, the feature modeling capability of the deep neural network is utilized to make up for the deficiency of the priori knowledge, it is not needed to display and establish an atmospheric scattering model in the deep neural network, but the atmospheric scattering model is regarded as condition generation of an image, and the defogging effect is good.

The invention relates to an image defogging method and system based on superpixel segmentation, a storage medium and electronic equipment, and the method comprises the steps: S1, obtaining a global atmospheric light value corresponding to a haze image, and carrying out the segmentation of the haze image through superpixel segmentation, so as to obtain a superpixel set corresponding to the haze image; S2, obtaining an initial transmittance graph corresponding to the haze image through a preset cost function based on the superpixel set; S3, performing refinement processing on the initial transmissivity graph to obtain a target transmissivity graph; and S4, obtaining a clear image corresponding to the haze image according to the target transmissivity image and the global atmospheric light value. By implementing the method, a good defogging effect on a real haze image and a synthetic haze image data set can be achieved.

The invention discloses an image defogging method based on prior information. The method comprises the following steps: estimating an atmospheric light value according to dark channel prior in combination with distant view dense fog area pixel points; respectively determining atmospheric transmissivity corresponding to distant view dense fog area pixel points and close view thin fog area pixel points according to color attenuation priori; determining atmospheric transmissivity corresponding to pixel points in the close-range mist area according to dark channel prior; determining pixel values corresponding to pixel points of the defogged distant view dense fog area and pixel values corresponding to pixel points of the defogged close view thin fog area; and performing region combination on the pixel values corresponding to the defogged distant view dense fog region pixel points and the defogged close view thin fog region pixel points to obtain a defogged image. By improving the defects of dark channel prior and color attenuation prior defogging algorithms in defogging application, the defogging method has a good defogging effect on mist images in various scenes.

The antifogging agent composition consists of polyethylene glycol of different chain lengths as non-ionic surfactant to make the interface film between oil and water firm and stable, modified natural polymer as filming material to prolong the acting period of the antifogging agent, silicon oil to orient the hydrophilic groups in surfactant and to resist reflection and multi-hydroxyl organic compound as antifreezing agent. It is sprayed onto the surface of glass and other matter with smooth surface to produce antifogging effect as long as 240 hr normally or 168 hr in extreme condition. In addition, it has also antifouling and antistatic functions.