Showing papers on "Edge enhancement published in 2015"

Weighted guided image filtering.

Abstract: It is known that local filtering-based edge preserving smoothing techniques suffer from halo artifacts. In this paper, a weighted guided image filter (WGIF) is introduced by incorporating an edge-aware weighting into an existing guided image filter (GIF) to address the problem. The WGIF inherits advantages of both global and local smoothing filters in the sense that: 1) the complexity of the WGIF is O(N) for an image with N pixels, which is same as the GIF and 2) the WGIF can avoid halo artifacts like the existing global smoothing filters. The WGIF is applied for single image detail enhancement, single image haze removal, and fusion of differently exposed images. Experimental results show that the resultant algorithms produce images with better visual quality and at the same time halo artifacts can be reduced/avoided from appearing in the final images with negligible increment on running times.

A low-light image enhancement method for both denoising and contrast enlarging

Abstract: In this paper, a novel united low-light image enhancement framework for both contrast enhancement and denoising is proposed. First, the low-light image is segmented into superpixels, and the ratio between the local standard deviation and the local gradients is utilized to estimate the noise-texture level of each superpixel. Then the image is inverted to be processed in the following steps. Based on the noise-texture level, a smooth base layer is adaptively extracted by the BM3D filter, and another detail layer is extracted by the first order differential of the inverted image and smoothed with the structural filter. These two layers are adaptively combined to get a noise-free and detail-preserved image. At last, an adaptive enhancement parameter is adopt into the dark channel prior dehazing process to enlarge contrast and prevent over/under enhancement. Experimental results demonstrate that our proposed method outperforms traditional methods in both subjective and objective assessments.

High Dynamic Range Image Compression by Optimizing Tone Mapped Image Quality Index

Abstract: Tone mapping operators (TMOs) aim to compress high dynamic range (HDR) images to low dynamic range (LDR) ones so as to visualize HDR images on standard displays. Most existing TMOs were demonstrated on specific examples without being thoroughly evaluated using well-designed and subject-validated image quality assessment models. A recently proposed tone mapped image quality index (TMQI) made one of the first attempts on objective quality assessment of tone mapped images. Here, we propose a substantially different approach to design TMO. Instead of using any predefined systematic computational structure for tone mapping (such as analytic image transformations and/or explicit contrast/edge enhancement), we directly navigate in the space of all images, searching for the image that optimizes an improved TMQI. In particular, we first improve the two building blocks in TMQI—structural fidelity and statistical naturalness components—leading to a TMQI-II metric. We then propose an iterative algorithm that alternatively improves the structural fidelity and statistical naturalness of the resulting image. Numerical and subjective experiments demonstrate that the proposed algorithm consistently produces better quality tone mapped images even when the initial images of the iteration are created by the most competitive TMOs. Meanwhile, these results also validate the superiority of TMQI-II over TMQI. 1 1 Partial preliminary results of this work were presented at ICASSP 2013 and ICME 2014.

Single underwater image enhancement using depth estimation based on blurriness

Abstract: In this paper, we propose to use image blurriness to estimate the depth map for underwater image enhancement. It is based on the observation that objects farther from the camera are more blurry for underwater images. Adopting image blurriness with the image formation model (IFM), we can estimate the distance between scene points and the camera and thereby recover and enhance underwater images. Experimental results on enhancing such images in different lighting conditions demonstrate the proposed method performs better than other IFM-based enhancement methods.

Mutual-Structure for Joint Filtering

Abstract: Previous joint/guided filters directly transfer the structural information in the reference image to the target one. In this paper, we first analyze its major drawback -- that is, there may be completely different edges in the two images. Simply passing all patterns to the target could introduce significant errors. To address this issue, we propose the concept of mutual-structure, which refers to the structural information that is contained in both images and thus can be safely enhanced by joint filtering, and an untraditional objective function that can be efficiently optimized to yield mutual structure. Our method results in necessary and important edge preserving, which greatly benefits depth completion, optical flow estimation, image enhancement, stereo matching, to name a few.

Synchronized multiartifact reduction with tomographic reconstruction (SMART-RECON): A statistical model based iterative image reconstruction method to eliminate limited-view artifacts and to mitigate the temporal-average artifacts in time-resolved CT.

Abstract: Purpose: In x-ray computed tomography (CT), a violation of the Tuy data sufficiency condition leads to limited-view artifacts. In some applications, it is desirable to use data corresponding to a narrow temporal window to reconstruct images with reduced temporal-average artifacts. However, the need to reduce temporal-average artifacts in practice may result in a violation of the Tuy condition and thus undesirable limited-view artifacts. In this paper, the authors present a new iterative reconstruction method, synchronized multiartifact reduction with tomographic reconstruction (SMART-RECON), to eliminate limited-view artifacts using data acquired within an ultranarrow temporal window that severely violates the Tuy condition. Methods: In time-resolved contrast enhanced CT acquisitions, image contrast dynamically changes during data acquisition. Each image reconstructed from data acquired in a given temporal window represents one time frame and can be denoted as an image vector. Conventionally, each individual time frame is reconstructed independently. In this paper, all image frames are grouped into a spatial–temporal image matrix and are reconstructed together. Rather than the spatial and/or temporal smoothing regularizers commonly used in iterative image reconstruction, the nuclear norm of the spatial–temporal image matrix is used in SMART-RECON to regularize the reconstruction of all image time frames. This regularizer exploits the low-dimensional structure of the spatial–temporal image matrix to mitigate limited-view artifacts when an ultranarrow temporal window is desired in some applications to reduce temporal-average artifacts. Both numerical simulations in two dimensional image slices with known ground truth and in vivo human subject data acquired in a contrast enhanced cone beam CT exam have been used to validate the proposed SMART-RECON algorithm and to demonstrate the initial performance of the algorithm. Reconstruction errors and temporal fidelity of the reconstructed images were quantified using the relative root mean square error (rRMSE) and the universal quality index (UQI) in numerical simulations. The performance of the SMART-RECON algorithm was compared with that of the prior image constrained compressed sensing (PICCS) reconstruction quantitatively in simulations and qualitatively in human subject exam. Results: In numerical simulations, the 240∘ short scan angular span was divided into four consecutive 60∘ angular subsectors. SMART-RECON enables four high temporal fidelity images without limited-view artifacts. The average rRMSE is 16% and UQIs are 0.96 and 0.95 for the two local regions of interest, respectively. In contrast, the corresponding average rRMSE and UQIs are 25%, 0.78, and 0.81, respectively, for the PICCS reconstruction. Note that only one filtered backprojection image can be reconstructed from the same data set with an average rRMSE and UQIs are 45%, 0.71, and 0.79, respectively, to benchmark reconstruction accuracies. For in vivo contrast enhanced cone beam CT data acquired from a short scan angular span of 200∘, three 66∘ angular subsectors were used in SMART-RECON. The results demonstrated clear contrast difference in three SMART-RECON reconstructed image volumes without limited-view artifacts. In contrast, for the same angular sectors, PICCS cannot reconstruct images without limited-view artifacts and with clear contrast difference in three reconstructed image volumes. Conclusions: In time-resolved CT, the proposed SMART-RECON method provides a new method to eliminate limited-view artifacts using data acquired in an ultranarrow temporal window, which corresponds to approximately 60∘ angular subsectors.

Gradual edge enhancement in spiral phase contrast imaging with fractional vortex filters.

Abstract: In the spiral phase contrast imaging, the integer spiral phase plate (SPP) are generally employed to perform the radial Hilbert transform on the object. Here we introduce fractional SPP filters, instead of the integer ones, to investigate the gradual formation of edge enhancement for pure phase objects. Two spatial light modulators are used in our experimental configuration. One is addressed to display the pure phase object of a five-pointed star, while the other serves as a dynamic filter of fractional topological charge Q. Of interest is the observation of the complete reversal of the edge and background brightness by gradually changing the fractional vortices from Q = 0 to 1. The experimental results were well interpreted based on the OAM spectra of fractional SPP, which indicates that the filtered output image can be considered as a coherent superposition of all possible images that are individually resulted from the integer OAM filtering. Besides, we show that the spiral phase contrast effect can still be observed in real time for a rotating three-leaf clover. Our results may find potential applications in the optical microscopic imaging.

Image Enhancement Techniques: A Study

Abstract: Image enhancement is considered as one of the most important techniques in image research. The main aim of image enhancement is to enhance the quality and visual appearance of an image, or to provide a better transform representation for future automated image processing. Many images like medical images, satellite, aerial images and also real life photographs suffer from poor and bad contrast and noise. It is necessary to enhance the contrast and remove the noise to increase image quality. One of the most important stages in medical images detection and analysis is Image Enhancement Techniques. It improves the clarity of images for human viewing, removing blurring and noise, increasing contrast, and revealing details. These are examples of enhancement operations. The enhancement technique differs from one field to another depending on its objective. The existing techniques of image enhancement can be classified into two categories: Spatial Domain and Frequency Domain Enhancement. In this paper, we present an overview of Image Enhancement Processing Techniques in Spatial Domain. More specifically, we categorise processing methods based representative techniques of Image enhancement. Thus the contribution of this paper is to classify and review Image Enhancement Processing Techniques as well as various noises has been applied to the image. Also we applied various filters to identify which filter is efficient in removing particular noises. This is identified by comparing the values obtained in PSNR and MSE values. From this we can get an idea about which filters is best for removing which types of noises. It will be useful and easier to detect the filters for future research.

Methods of Image Edge Detection: A Review

Abstract: The first step in an image recognition system is the edges sensibility in a digital image. Edge detection for object observation in image processing is the important part. This will give us a good understanding of edge detection algorithms. An edge is useful because it marks the boundaries and divides of plane, object or appearance from other places things. For pattern recognition it is also an intermediate step in the digital images. An edge consists of pixels with the intensity variations of gray tones which are different from their neighbour pixels. This paper introduces the standard edge detection methods which are widely used in image processing such as Prewitt, Laplacian of Gaussian, Canny, Sobel, Robert and also the new approach are discussed in this known as Fuzzy logic.

Edge Perpendicular Binary Coding for USM Sharpening Detection

Abstract: Unsharp masking (USM) sharpening is a basic technique for image manipulation and editing. In recent years, the detection of USM sharpening has attracted attention from image forensics point of view. After USM sharpening, overshoot artifacts, which shape image texture, are generated along image edges. By utilizing the special characteristic of the texture modification caused by the USM sharpening, a novel method called edge perpendicular binary coding is proposed in this letter to detect USM sharpening. Extensive experiments have been conducted to show the superiority of the proposed method over the existing methods.

An Adaptive Histogram Equalization Based Local Technique for Contrast Preserving Image Enhancement

Abstract: The main purpose of image enhancement is to improve certain characteristics of an image to improve its visual quality. This paper proposes a method for image contrast enhancement that can be applied to both medical and natural images. The proposed algorithm is designed to achieve contrast enhancement while also preserving the local image details. To achieve this, the proposed method combines local image contrast preserving dynamic range compression and contrast limited adaptive histogram equalization (CLAHE). Global gain parameters for contrast enhancement are inadequate for preserving local image details. Therefore, in the proposed method, in order to preserve local image details, local contrast enhancement at any pixel position is performed based on the corresponding local gain parameter, which is calculated according to the current pixel neighborhood edge density. Different image quality measures are used for evaluating the performance of the proposed method. Experimental results show that the proposed method provides more information about the image details, which can help facilitate further image analysis.

Efficient image sharpening and denoising using adaptive guided image filtering

Abstract: Enhancing the sharpness and reducing the noise of blurred, noisy images are crucial functions of image processing. Widely used unsharp masking filter-based approaches suffer from halo-artefacts and/or noise amplification, while noise- and halo-free adaptive bilateral filtering (ABF) is computationally intractable. In this study, the authors present an efficient sharpening algorithm inspired by guided image filtering (GF). The author's proposed adaptive GF (AGF) integrates the shift-variant technique, a part of ABF, into a guided filter to render crisp and sharpened outputs. Experiments showed the superiority of their proposed algorithm to existing algorithms. The proposed AGF sharply enhances edges and textures without causing halo-artefacts or noise amplification, and it is efficiently implemented using a fast linear-time algorithm.

A combined effect of local and global method for contrast image enhancement

Abstract: Image enhancement is used to improve the digital quality of image. It is used to improve the poor quality of image that is too used to improve bad quality of picture into good picture or image. This paper suggests a combination of local and global method for contrast image enhancement. Global contrast image enhancement improves low contrast of image in a global way. This type of global enhancement avoids noise and other ringing artifacts of a digital image. In global contrast image enhancement when high contrast occurs it causes under exposure on some part of image and over exposure on some other part of an image. Global contrast image enhancement has much advantage but it lack in local enhancement of image means it lacks the local detail of an image. When we use local detail of an image, the local detail of an image can be defined in better way. Local contrast image enhancement increases noise of an image when high contrast gain occurs. When we use global contrast image enhancement or local contrast image enhancement single handedly it is not beneficial but when we use combination of local and global method it gives us better results for certain images. In this paper we will going to use global contrast stretching method for global contrast image enhancement. In local contrast image enhancement method we are using unsharp masking technique to enhance the local detail of an image. The main aim of using this combination of local and global method is to preserve the brightness of an image when contrast image enhancement is done.

Edge enhancement of potential field data using an enhanced tilt angle

Abstract: We present an edge-detection technique for the enhancement of potential field data, which is based on the tilt angle of the first order vertical derivative of the total horizontal gradient. The tec...

Edge enhancement for thermal-visible combined images and cameras

Abstract: Systems and methods directed toward combining visible light and infrared images can include processing visible light image data to determine an edge factor value for a plurality of visible light pixels corresponding to the strength of an edge at that location The edge factor value can be determined using features from the visible light image data and an edge gain input, which may be adjustable by a user The edge factor values are combined with an edge midscale value to create a first set of modified visible light image data including pixels emphasized based on the strength of the edge in the visible light image The modified visible light image data is combined with infrared image data to create combined image data having contribution from the infrared image data and the edge factor values from the visible light image data

A critical look to some contrast enhancement evaluation measures

Abstract: Contrast enhancement is one of the most studied problems in image processing. A plethora of approaches has been proposed in the literature for image enhancement since the pioneer work of Kovasznay and Joseph in 1955 [1] and the famous contribution of Gabor in 1965 on image deblurring [2]. However, very few works have been dedicated to contrast enhancement evaluation. This is mainly due to the fact that image enhancement is primarily related to subjective aspects of human perceptual vision. The intent of this paper is to provide a first critical review to some contrast enhancement evaluation measures and propose a new one. An objective comparison of these measures on various color real images processed by some neighborhood based methods is provided. This work is considered as a first step towards the development of a unifying framework for image enhancement evaluation. This could be also used to control the side effect that may result from any image enhancement such as contrast enhancement, denoising, tone mapping and other similar image processing tools.

Philippine currency paper bill counterfeit detection through image processing using Canny Edge Technology

Abstract: Image edge information is essentially one of the most significant information in an image, which can describe the target outline, its relative position within the target area, and other important information. Edge detection is one of the most important process in image processing, and the detection results directly affects the image analysis. Traditional edge detection algorithms are accomplished through detecting the maximum value of the first derivative or zero crossing of the second derivative. Though it seems the representative first order differential operators have known advantages like simple computation, speed and ease of implementation, they are more sensitive to noise and their detection effect are not perfect in most engineering application. Schemes for counterfeit detection has been continuously evolving through various techniques and algorithms run on machines and devices. This paper presents image enhancement and image sensing through the use of Canny Edge Technology. Moreover, counterfeit detection on selected Philippine banknotes was achieved by incorporating a distinct security feature known as Optically Variable Device (OVD) patch, as an improvement to the traditional three-way detection of bills (watermark, security thread, see-through marks)A MATLAB GUI program was developed that acquired and processed the image through Canny Edge Technology. The Canny Edge algorithm proved a robust method in detecting fake bills through OVD security features, revealing statistically significant detection proportion under a 5% level of significant for the four cases under test.

Text Detection Using MSER and Stroke Width Transform

Abstract: Text extraction, detection and recognition in images aims at integrating advanced text-based searching technologies which is now recognized as a main component in the development of advanced image and video annotation and retrieval systems. It is also used for image in painting. In this paper, we present a novel algorithm for detecting text in natural and complex images. Basically, it is a two step approach combining maximally stable extremal regions (MSER) and stroke width transformation for the accurate detection of text. Firstly, the MSER image is obtained on which canny edge detection is performed for edge enhancement. To exclude non-text parts, the image is filtered using stroke width information as well as geometric filtering. Experimental results show the effective performance of the proposed method.

Deblurred Images Post-Processing by Poisson Warping

Abstract: In this work we develop a post-processing algorithm which enhances the results of the existing image deblurring methods. It performs additional edge sharpening using grid warping. The idea of the proposed algorithm is to transform the neighborhood of the edge so that the neighboring pixels move closer to the edge, and then resample the image from the warped grid to the original uniform grid. The proposed technique preserves image textures while making the edges sharper. The effectiveness of the method is shown for basic deblurring methods on LIVE database images with added blur and noise.

Super-Resolution Image Reconstruction Using Wavelet Based Patch and Discrete Wavelet Transform

Abstract: This paper proposes a novel method that combines the discrete wavelet transform (DWT) and example-based technique to reconstruct a high-resolution from a low-resolution image. Although previous interpolation- and example-based methods consider the reconstruction adaptive to edge directions, they still have a problem with aliasing and blurring effects around edges. In order to address these problems, in this paper, we utilize the frequency sub-bands of the DWT that has the feature of lossless compression. Our proposed method first extracts the frequency sub-bands (Low-Low, Low-High, High-Low, High-High) from an input low-resolution image by the DWT, and then the low-resolution image is inserted into the Low-Low sub-band. Since information in high-frequency sub-bands (Low-High, High-Low, and High-High) might be lost in the low-resolution image, they are reconstructed or estimated by using example-based method from image patch database. After that, we make a high-resolution image by performing the inverse DWT of reconstructed frequency sub-bands. In experimental results, we can show that the proposed method outperforms previous approaches in terms of edge enhancement, reduced aliasing effects, and reduced blurring effects.

Stain detection and classification method and device for sensor of digital camera

Abstract: The invention discloses a stain detection method for a sensor of a digital camera, and a method and a device for classifying the sensor of the camera based on the detection method, wherein the method comprises the following steps: (1) inputting original image data and obtaining luminance component via interpolation, (2) low-pass filtering, (3) edge enhancement, (4) band-pass filtering, (5) image binaryzation operation and morphological dilation, (6) connectivity area abstraction, (7) marking stain in an original image, (8) classifying the sensor and outputting image level. The invention is divided as two function modules which are used for detecting the stain and classifying the sensor. A contiguous item of a classification decision function is from features such as quantity, area, and color depth of the stain detected in a shooting image. The method and the device of the invention is simple and efficient and can be used for fast detecting stain position on the sensor and carrying out accurate level evaluation to current sensor according to the feature of theses stains.

Least-squares images for edge-preserving smoothing

Abstract: In this paper, we propose least-squares images (LS-images) as a basis for a novel edge-preserving image smoothing method. The LS-image requires the value of each pixel to be a convex linear combination of its neighbors, i.e., to have zero Laplacian, and to approximate the original image in a least-squares sense. The edge-preserving property inherits from the edge-aware weights for constructing the linear combination. Experimental results demonstrate that the proposed method achieves high quality results compared to previous state-of-the-art works. We also show diverse applications of LS-images, such as detail manipulation, edge enhancement, and clip-art JPEG artifact removal.

Edge enhancement of magnetic data using fractional-order-derivative filters

Abstract: Edge enhancement and detection techniques are fundamental operations in magnetic data interpretation. Many techniques for edge enhancement have been developed, some based on profile data and others designed for grid-based data sets. Methods that are traditionally applied to magnetic data, such as total horizontal derivative (THD) and analytic signal (AS), require the computation of integer-order horizontal and vertical derivatives of the magnetic data. However, if the data set contains features with a large variation in amplitude, then the features with small amplitudes may be difficult to outline. In addition, because most edge enhancement and detection filters are derivative-based filters, they also amplify high-frequency noise content in the data. As a result, the accuracy of derivative-based filters is restricted to data of high quality. We suggested the modification of the THD and AS filters by combining the amplitude spectra of fractional-order-derivative filters with ad hoc phase spectra, p...

Image enhancement techniques: a review

Abstract: Field of image processing has vast applications in medical, forensic, research etc., It includes various domains like enhancement, classification, segmentation, etc., which are widely used for these applications. Image Enhancement is the pre processing step on which the accuracy of the result lies. Image enhancement aims to improve the visual appearance of an image, without affecting the original attributes (i.e.,) image contrast is adjusted and noise is removed to produce better quality image. Hence image enhancement is one of the most important tasks in image processing. Enhancement is classified into two categories spatial domain enhancement and frequency domain enhancement. Spatial domain enhancement acts upon pixel value whereas frequency domain enhancement acts on the Fourier transform of the image. The enhancement techniques to be used depend on modality, climatic and visual perspective etc., In this paper, we present a survey on various existing image enhancement techniques.

Color Image Enhancement using Edge Based Histogram Equalization

Abstract: Image enhancement is one of the major research areas in the field of digital image processing. The sole objective of this domain is to enhance the quality of a poor contrast image. As a result, the final processed image becomes much more understandable than the original one. A new edge based histogram equalization method is proposed in this study. A high pass filter is used to detect edges with a help of an appropriate gradient operator. In general, a convolution mask is used for this kind of area processes like filtering. This proposed method increases the quality of the poor contrast area. This method does not create an impact on brighter area in the given input image. A few portion of the poor contrast is raised and few others contrast are being reduced. The simulation results of this study are compared with the conventional histogram equalization. Our experimental results show that the Peak Signal-to-Noise Ratio (PSNR) is better than the regular Histogram Equalization (HE) method. All simulation results are obtained using Matlab simulation software tool with standard color test images. Color image enhancement methods are applicable in the areas such as iris recognition, digital photography, remote sensing, biology, medicine, geophysics and microarray techniques, etc.

Morphological and geometric edge filters for edge enhancement in depth images

Abstract: A mechanism is described for facilitating three-dimensional (3D) depth imaging systems, and morphological and geometric filters for edge enhancement in depth images at computing devices according to one embodiment. A method of embodiments, as described herein, includes detecting an input digital image of an object, the digital image comprising data pixels contaminated by noise and confidence values corresponding to the data pixels, and computing a morphological filter by matching the confidence pixels in the input digital image with a set of matching templates, and using a set of masking templates to determine the data pixels and confidence pixels in the filtered image. The method further include computing an edge filter by performing computation of distances between the data pixels along a plurality of directions to determine an edge direction, and determining the data pixels and and the confidence pixels in a filtered image based on the edge direction. The method may further include applying at least one of the morphological filter and the edge filter to filter the digital image.

Method and device for processing image noise

Abstract: The invention provides a method and a device for processing image noise. The method comprises the steps of: obtaining a Y-component image of a YUV space image corresponding to a frame of original image; decomposing the Y-component image by adopting a preset image decomposition algorithm to obtain a corresponding high frequency domain signal and a corresponding low frequency domain signal; identifying an edge signal and a non-edge signal in the high frequency domain signal; carrying out edge enhancement processing for the edge signal and filtering the non-edge signal by using a first filtering method to obtain the processed high frequency domain signal; filtering the low frequency domain signal by using a second filtering method to obtain the processed low frequency domain signal; reconstructing the processed high frequency domain signal and the processed low frequency domain signal by using an image reconstruction algorithm which corresponds to the preset image decomposition algorithm so as to obtain a de-noised Y-component image. The method for removing the image noise, provided by the invention, can efficiently remove a plenty of noise in the image, and can improve Signal to Noise Ratio (SNR) of the image.

Analysis and application of image segmentation and edge detection operator

Abstract: Edges are the main feature of image, also an essential part of the computer visual and pattern recognition, so edge detection is a crucial step in the process of image processing. This article analyzes the algorithm of image segmentation and edge detection, compared the advantages and disadvantages of various operators by MATLAB. Through the experimental comparison, The overall effect of Canny operator is relatively well, but less detailed. So there is not an universal edge operator. The most important is how to choose a suitable threshold, this will be a decisive role.

Rendering high quality images via micro-segmentation, error diffusion, and edge enhancement

Abstract: Techniques related to rendering scanned images are discussed. Such techniques may include selectively processing segments of a scanned input image based on a neutral edge tag signal such that segments are color suppressed and edge enhanced when the neutral edge tag signal is asserted and error diffusion processed when the neutral edge tag signal is not asserted.

Adaptive denoising method for ultrasonic image

Abstract: The invention discloses an adaptive denoising method for an ultrasonic image. The adaptive denoising method comprises the following steps: (1) obtaining the ultrasonic image; (2) performing Gaussian-Laplacian pyramid decomposition on the ultrasonic image and obtaining Gaussian layers and Laplacian layers at different scales; (3) calculating a structure tensor and a diffusion tensor of the Gaussian layer at each scale and performing anisotropic diffusion filtering treatment on the Gaussian layer at the scale; (4) according to a characteristic value of the structure tensor of the Gaussian layer, designing a grey mapping curve, and performing grey mapping on the Gaussian layer at the scale according to the grey mapping curve; (5) repeating the steps (3) and (4) for multiple times and performing the same treatment on the Gaussian layer and the Laplacian layer at each scale; (6) performing reverse reconstruction on the treated Gaussian layer and Laplacian layer and obtaining a denoised ultrasonic image; (7) outputting the denoised ultrasonic image. The adaptive denoising method can inhibit edge enhancement and spots of the image and is simple in algorithm and strong in adaptability.