A Small Target Detection Method for Sea Surface Based on Guided Filtering and Local Mean Gray Difference

The traditional small target detection algorithm often results in a high false alarm rate on the sea surface background. To address this issue, a small target detection method based on guided filtering and local average gray level difference was proposed in this paper for the sea surface. Firstly, the method enhanced the details of the small targets by employing guided filtering to suppress the background clutter and noise in the sea surface image. Subsequently, the local average gray level difference of each point in the image was calculated to further distinguish the targets from other interference points. Finally, the threshold segmentation method was utilized to obtain the actual small targets on the sea surface. After conducting experiments on various sea surface scenes, the LSCRG, BSF, and ROC curve were computed for the proposed method and five other algorithms. Comparative analysis with BS, Top-hat, TDLMS, Max-median, and LCM demonstrates the superiority of the proposed method for infrared small target detection on the sea surface.

Static-shift suppression and anti-interference signal processing for CSAMT based on Guided Image Filtering

Shallow conductive heterogeneity can lead to static shifts ain the apparent resistivity sounding curve of controlled-source audio-frequency magnetotellurics(CSAMT).The static effect will shift the apparent resistivity curves along with axial log-log coordinates.Such an effect,if not properly processed,can distort the resistivity of rock formation and the depth of interfaces,and even make the geological structures unrecognizable.In this paper,we discuss the reasons and characteristics of the static shift and summarize the previous studies regarding static shift correction.Then,we propose the Guided Image Filtering algorithm to suppress static shifts in CSAMT.In detail,we use the multi-window superposition method to superimpose 1D signals into a 2D matrix image,which is subsequently processed with Guided Image Filtering.In the synthetic model study and field examples,the Guided Image Filtering algorithm has effectively corrected and suppressed static shifts,and finally improved the precision of data interpretation.

Underwater Diver Image Enhancement via Dual-Guided Filtering

The scattering and absorption of light propagating underwater cause the underwater images to present lowcontrast,color deviation,and loss of details,which in turn make human posture recognition challenging.To address these issues,this study introduced the dual-guided filtering technique and developed an underwater diver image improvement method.First,the color distortion of the underwater diver image was solved using white balance technology to obtain a color-corrected image.Second,dual-guided filtering was applied to the white balanced image to correct the distorted color and enhance its details.Four feature weight maps of the two images were then calculated,and two normalizedweightmapswere constructed formulti-scale fusion using normalization.To better preserve the obtained image details,the fusion image was histogram-stretched to obtain the final enhanced result.The experimental results validated that this method has improved the accuracy of underwater human posture recognition.

SAR image de-noising via grouping-based PCA and guided filter

A novel synthetic aperture radar(SAR)image de-noising method based on the local pixel grouping(LPG)principal component analysis(PCA)and guided filter is proposed.This method contains two steps.In the first step,we process the noisy image by coarse filters,which can suppress the speckle effectively.The original SAR image is transformed into the additive noise model by logarithmic transform with deviation correction.Then,we use the pixel and its nearest neighbors as a vector to select training samples from the local window by LPG based on the block similar matching.The LPG method ensures that only the similar sample patches are used in the local statistical calculation of PCA transform estimation,so that the local features of the image can be well preserved after coefficients shrinkage in the PCA domain.In the second step,we do the guided filtering which can effectively eliminate small artifacts left over from the coarse filtering.Experimental results of simulated and real SAR images show that the proposed method outstrips the state-of-the-art image de-noising methods in the peak signalto-noise ratio(PSNR),the structural similarity(SSIM)index and the equivalent number of looks(ENLs),and is of perceived image quality.

Low and non-uniform illumination color image enhancement using weighted guided image filtering

In the state of the art,grayscale image enhancement algorithms are typically adopted for enhancement of RGB color images captured with low or non-uniform illumination.As these methods are applied to each RGB channel independently,imbalanced inter-channel enhancements(color distortion)can often be observed in the resulting images.On the other hand,images with non-uniform illumination enhanced by the retinex algorithm are prone to artifacts such as local blurring,halos,and over-enhancement.To address these problems,an improved RGB color image enhancement method is proposed for images captured under nonuniform illumination or in poor visibility,based on weighted guided image filtering(WGIF).Unlike the conventional retinex algorithm and its variants,WGIF uses a surround function instead of a Gaussian filter to estimate the illumination component;it avoids local blurring and halo artifacts due to its anisotropy and adaptive local regularization.To limit color distortion,RGB images are first converted to HSI(hue,saturation,intensity)color space,where only the intensity channel is enhanced,before being converted back to RGB space by a linear color restoration algorithm.Experimental results show that the proposed method is effective for both RGB color and grayscale images captured under low exposure and non-uniform illumination,with better visual quality and objective evaluation scores than from comparator algorithms.It is also efficient due to use of a linear color restoration algorithm.

Retinex based low-light image enhancement using guided filtering and variational framework

A new image enhancement algorithm based on Retinex theory is proposed to solve the problem of bad visual effect of an image in low-light conditions. First, an image is converted from the RGB color space to the HSV color space to get the V channel. Next, the illuminations are respectively estimated by the guided filtering and the variational framework on the V channel and combined into a new illumination by average gradient. The new reflectance is calculated using V channel and the new illumination. Then a new V channel obtained by multiplying the new illumination and reflectance is processed with contrast limited adaptive histogram equalization(CLAHE). Finally, the new image in HSV space is converted back to RGB space to obtain the enhanced image. Experimental results show that the proposed method has better subjective quality and objective quality than existing methods.

Color image detail enhancement based on quaternion guided filter

Color image enhancement is an active research field in image processing. Currently, many image enhancement methods are capable of enhancing the details of the color image. However, these methods only process the red, green and blue （RGB） color channels separately, which leads to color distortion easily. In order to overcome this problem, the paper presents an approach to integrate the quaternion theory into the traditional guided filter to obtain a quaternion guided filter （QGF）. This method makes full use of the color information of an image to realize the holistic processing of RGB color channels. So as to preserve color information while enhancing details, this paper proposes a color image detail enhancement algorithm based on the QGF. Experimental results show that the proposed algorithm is effective in the applications of the color image detail enhancement, and enables image＇s edges to be more prominent and texture clearer while avoiding color distortion. Compared with the existing image enhancement methods, the proposed method achieves better enhancement performance in terms of the visual quality and the objective evaluating indicators.

Edit Propagation via Edge-Aware Filtering

This paper presents a novel framework for efficiently propagating the stroke-based user edits to the regions with similar colors and locations in high resolution images and videos. Our framework is based on the key observation that the edit propagation intrinsically can also be achieved by utilizing recently proposed edge-preserving filters. Therefore, instead of adopting the traditional global optimization which may involve a time-consuming solution, our algorithm propagates edits with the aid of the edge-preserve filters. Such a propagation scheme has low computational complexity and supports multiple kinds of strokes for more flexible user interactions. Further, our method can be easily and efficiently implemented in GPU. The experimental results demonstrate the efficiency and user-friendliness of our approach.

Efficient mesh denoising via robust normal filtering and alternate vertex updating

The most challenging problem in mesh denoising is to distinguish features from noise. Based on the robust guided normal estimation and alternate vertex updating strategy, we investigate a new feature-preserving mesh denoising method. To accurately capture local structures around features, we propose a corner-aware neighborhood （CAN） scheme. By combining both overall normal distribution of all faces in a CAN and individual normal influence of the interested face, wc give a new consistency measuring method, which greatly improves the reliability of the estimated guided normals. As the noise level lowers, we take as guidance the previous filtered normals, which coincides with the emerging roUing guidance idea. In the vertex updating process, we classify vertices according to filtered normals at each iteration and reposition vertices of distinct types alternately with individual regularization constraints. Experiments on a variety of synthetic and real data indicate that our method adapts to various noise, both Gaussian and impulsive, no matter in the normal direction or in a random direction, with fcw triangles flippcd.

Optimized guided mode resonant structure as thermooptic sensor and liquid crystal tunable filter

Applicability of guided mode resonant structures to tunable optical filtering and sensing is demonstrated using nematic liquid crystals. As a sensor, a minimum refractive index detectivity of 10^-5 is demonstrated while as a tunable filter, tunability range of few tens of nanometers with 2-nm bandwidth is presented. The optimum design is achieved by maximizing the evanescent field region in the analyte which maximizes the overlap integral. The device can be operated in reflection or transmission modes at normal incidence. It can also be operated at a single wavelength by measuring the angular profile of the light beam.

Infrared image enhancement based on adaptive weighted guided filter

The physical principle of infrared imaging leads to the low contrast of the whole image,the blurring of contour and edge details,and it is also sensitive to noise.To improve the quality of infrared image and visual effect,an adaptive weighted guided filter(AWGF) for infrared image enhancement algorithm was proposed.The core idea of AWGF algorithm is to propose an adaptive strategy to update the weights of guided filter(GF) parameters,which not only improves the accuracy of regularization parameter estimation in GF theory,but also achieves the purpose of removing infrared image noise and improving its detail contrast.A large number of real infrared images were used to verify AWGF algorithm,and good experimental results were obtained.Compared with other guided filtering algorithms,the halo phenomenon at the edge of infrared images processed by the AWGF algorithm is significantly avoided,and the evaluation parameter values of information entropy(IE),average gradient(AG),and moment of inertia(MI)are relatively high.This shows that the quality of infrared image processed by the AWGF algorithm is better.

Image Dehazing with Hybrid λ2-λ0 Penalty Mode

Due to the presence of turbid media, such as microdust and water vapor in the environment, outdoor pictures taken under hazy weather circumstances are typically degraded. To enhance the quality of such images, this work proposes a new hybrid λ2-λ0 penalty model for image dehazing. This model performs a weighted fusion of two distinct transmission maps, generated by imposing λ2 and λ0 norm penalties on the approximate regression coefficients of the transmission map. This approach effectively balances the sparsity and smoothness associated with the λ0 and λ2 norms, thereby optimizing the transmittance map. Specifically, when the λ2 norm is penalized in the model, an updated guided image is obtained after implementing λ0 penalty. The resulting optimization problem is effectively solved using the least square method and the alternating direction algorithm. The dehazing framework combines the advantages of λ2 and λ0 norms, enhancing sparse and smoothness, resulting in higher quality images with clearer details and preserved edges.

Image enhancement for crop trait information acquisition system

Collecting images using portable devices is an effective and convenient method for acquiring crop trait information.Because of uncertain environmental conditions in the field,enhancement is necessary to improve the visual quality of images.With this aim,here we propose an adaptive image enhancement method based on guided filtering.Our method automatically calculates the enhancement weights of the detail in an image according to the distribution characteristics of the illumination intensity of a crop image,so as to adaptively adjust the contrast of the image.To verify the effectiveness of the proposed algorithm,we performed enhancement experiments on 50 images of four kinds of cucumber leaf tissues,namely,leaves infected with target spot,powdery mildew,and downy mildew,and healthy leaves.The results showed that our proposed method substantially improved the visual quality of the images.Moreover,the mean ratios of the contrast to color difference obtained using the proposed method were higher than the mean ratios obtained using five conventional enhancement methods.We consider the proposed method for image enhancement will be a valuable addition to the crop trait information acquisition system(http://ebreed.com.cn/).

Low-light color image enhancement based on NSST

In order to improve the visibility and contrast of low-light images and better preserve the edge and details of images,a new low-light color image enhancement algorithm is proposed in this paper.The steps of the proposed algorithm are described as follows.First,the image is converted from the red,green and blue(RGB)color space to the hue,saturation and value(HSV)color space,and the histogram equalization(HE)is performed on the value component.Next,non-subsampled shearlet transform(NSST)is used on the value component to decompose the image into a low frequency sub-band and several high frequency sub-bands.Then,the low frequency sub-band and high frequency sub-bands are enhanced respectively by Gamma correction and improved guided image filtering(IGIF),and the enhanced value component is formed by inverse NSST transform.Finally,the image is converted back to the RGB color space to obtain the enhanced image.Experimental results show that the proposed method not only significantly improves the visibility and contrast,but also better preserves the edge and details of images.

Improved single image dehazing using dark channel prior

An improved single image dehazing method based on dark channel prior and wavelet transform is proposed. This proposed method employs wavelet transform and guided filter instead of the soft matting procedure to estimate and refine the depth map of haze images. Moreover, a contrast enhancement method based on just noticeable difference(JND) and quadratic function is adopted to enhance the contrast for the dehazed image, since the scene radiance is usually not as bright as the atmospheric light,and the dehazed image looks dim. The experimental results show that the proposed approach can effectively enhance the haze image and is well suitable for implementing on the surveillance and obstacle detection systems.