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.

Color Image Enhancement by an Integral Mask-filtering Approach Employing Nonlinear Transfer Function

We demonstrate a brand-new method to sharpen a color image by using an integral mask-filtering technique. The derivatives between the target pixel and its neighbors are transferred by the cubic root function instead of the traditional linear one. The obtained final image has clearer fine characteristics along with much less overshooting.

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.

Metaheuristic Based Noise Identification and Image Denoising Using Adaptive Block Selection Based Filtering

Image denoising has become one of the major forms of image enhancement methods that form the basis of image processing. Due to the inconsistencies in the machinery producing these signals, medical images tend to require these techniques. In real time, images do not contain a single noise, and instead they contain multiple types of noise distributions in several indistinct regions. This paper presents an image denoising method that uses Metaheuristics to perform noise identification. Adaptive block selection is used to identify and correct the noise contained in these blocks. Though the system uses a block selection scheme, modifications are performed on pixel- to-pixel basis and not on the entire blocks;hence the image accuracy is preserved. PSO is used to identify the noise distribution, and appropriate noise correction techniques are applied to denoise the images. Experiments were conducted using salt and pepper noise, Gaussian noise and a combination of both the noise in the same image. It was observed that the proposed method performed effectively on noise levels up-to 0.5 and was able to produce results with PSNR values ranging from 20 to 30 in most of the cases. Excellent reduction rates were observed on salt and pepper noise and moderate reduction rates were observed on Gaussian noise. Experimental results show that our proposed system has a wide range of applicability in any domain specific image denoising scenario, such as medical imaging, mammogram etc.

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.

Dim Moving Small Target Detection by Local and Global Variance Filtering on Temporal Profiles in Infrared Sequences

In this paper, the temporal different characteristics between the target and background pixels are used to detect dim moving targets in the slow-evolving complex background. A local and global variance filter on temporal profiles is presented that addresses the temporal characteristics of the target and background pixels to eliminate the large variation of background temporal profiles. Firstly, the temporal behaviors of different types of image pixels of practical infrared scenes are analyzed.Then, the new local and global variance filter is proposed. The baseline of the fluctuation level of background temporal profiles is obtained by using the local and global variance filter. The height of the target pulse signal is extracted by subtracting the baseline from the original temporal profiles. Finally, a new target detection criterion is designed. The proposed method is applied to detect dim and small targets in practical infrared sequence images. The experimental results show that the proposed algorithm has good detection performance for dim moving small targets in the complex background.

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.

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.

An Image Enhancement Method Based on Fractional Calculus and Retinex

In order to resolve the problem of the image degradation, an image enhancement method based on fractional calculus and Retinex is proposed, which can preserve or enhance texture information and remove the noise of images. The fractional differential is used to preprocess the input image to enhance texture information, and using guided filter to estimate the illumination component, so it has less halo phenomena. The reflection component, obtained according to the Retinex theory, is denoised by fractional integral to remove the noises. The image is equalized by the contrast limited adaptive histogram equalization to improve the image contrast, and a final enhanced image is obtained. The experimental results show that the method can effectively achieve image enhancement, and the enhanced image has better visual effects.

Enhancement algorithm for underwater weld seam image

It is hard to treat the underwater weld seam images for the reason of bad brightness, low contrast and less welding seam information, so a new enhancement algorithm is proposed here. Firstly, the high frequency component was separated by Gaussian filter from origin image, and then it is processed by improved local contrast enhancement(LCE) algorithm to enhance the edge information. Secondly, the gamma transform with adaptive parameters was used to strengthen the image brightness, furthermore, contrast limited adaptive histogram equalization(CLAHE) algorithm was applied to enhance the image contrast. Finally, the two manipulated images were integrated together to obtain the desired image. Experiments on typical images were carried out, and evaluation results showed that this designed algorithm can effectively improve image contrast, highlight welding seam information. Moreover, the image average grey value was moderate, and the information entropy and average gradient were much higher than other algorithms.

Multi-level denoising and enhancement method based on wavelet transform for mine monitoring

Based on low illumination and a large number of mixed noises contained in coal mine, denoising with one method usually cannot achieve good results, so a multi-level image denoising method based on wavelet correlation relevant inter-scale is presented. Firstly, we used directional median filter to effectively reduce impulse noise in the spatial domain, which is the main cause of noise in mine. Secondly, we used a Wiener filtration method to mainly reduce the Gaussian noise, and then finally used a multi-wavelet transform to minimize the remaining noise of low-light images in the transform domain. This multi-level image noise reduction method combines spatial and transform domain denoising to enhance benefits, and effectively reduce impulse noise and Gaussian noise in a coal mine, while retaining good detailed image characteristics of the underground for improving quality of images with mixing noise and effective low-light environment.

Target tracking in infrared imagery using a novel particle filter

To address two challenging problems in infrared target tracking, target appearance changes and unpre- dictable abrupt motions, a novel particle filtering based tracking algorithm is introduced. In this method, a novel saliency model is proposed to distinguish the salient target from background, and the eigenspace model is invoked to adapt target appearance changes. To account for the abrupt motions efficiently, a two- step sampling method is proposed to combine the two observation models. The proposed tracking method is demonstrated through two real infrared image sequences, which include the changes of luminance and size, and the drastic abrupt motions of the target.

Track before detect for point targets with particle filter in infrared image sequences

The problem of detecting and tracking point targets in a sequence of infrared images with very low signalto-noise ratio (SNR) is investigated in this paper. A track before detect algorithm for infrared (IR) point target is developed based on particle filter. The particle filter is used to estimate the state of the target in track stage. The unnormalized weights of the output of the filter are used to approximately construct the likelihood ratio for hypothesis test in detection stage. Experiment results with the real image sequences that SNR is about 2.0 show that the proposed algorithm can successfully detect and track point target.

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.

Enhanced quantitative X-ray phase-contrast images using Foucault differential filters

Several X-ray phase visualization methods are being real- ized for imaging of phase objects, such as biological and polymeric specimens. Grating-based phase-contrast imaging using a source-grating-attached X-ray tube that provides partially coherent X rays is one of the most successful methods in this field.

Fabrication of multi-wavelength visible and infrared filter for solar atmosphere tomographic imaging

To simultaneously obtain high-resolution multi-wavelength （from visible to near infrared） tomographic images of the solar atmosphere, a high-performance multi-wavelength optical filter has to be used in solar imaging telescopes. In this Letter, the fabrication of the multi-wavelength filter for solar tomographic imaging is described in detail. For this filter, Ta2O5 and SiO2 are used as high- and low-index materials, respectively, and the multilayer structure is optimized by commercial Optilayer software at a 7.5° angle of incidence. Experimentally, this multi-wavelength optical filter is prepared by a plasma ion-assisted deposition technique with optimized deposition parameters. High transmittance at 393.3, 396.8, 430.5, 525, 532.4, 656.8, 705.8, 854.2, 1083, and 1565.3 nm, as well as high reflectance at 500 and 589 nm are achieved. Excellent environmental durability, demonstrated via temperature and humidity tests, is also established.

Improved multilevel filters to enhance infrared small target

We propose improved multilevel filters （IMLFs） involving the absolute value operation into the algorithmic framework of traditional multilevel filters （MLFs） to improve the robustness of infrared small target enhancement techniques under a complex infrared cluttered background. Compared with the widely used small target enhancement methods which only deal with bright targets, the proposed technique can enhance the infrared small target, whether it is bright or dark. Experimental results verify that the proposed technique is efficient and practical.

Photographic Appearance Enhancement via Detail-Based Dictionary Learning

A number of edge-aware filters can efficiently boost the appearance of an image by detail decomposition and enhancement. However, they often fail to produce photographic enhanced appearance due to some visible artifacts, especially noise, halos and unnatural contrast. The essential reason is that the guidance and the constraint of high-quality appearance are not sufficient enough in the process of enhancement. Thus our idea is to train a detail dictionary from a lot of high-quality patches in order to constrain and control the entire appearance enhancement. In this paper, we propose a novel learning-based enhancement method for photographic appearance, which includes two main stages： dictionary training and sparse reconstruction. In the training stage, we construct a training set of detail patches extracted from some high-quality photos, and then train an overcomplete detail dictionary by iteratively minimizing an？1-norm energy function. In the reconstruction stage, we employ the trained dictionary to reconstruct the boosted detail layer, and further formalize a gradient-guided optimization function to improve the local coherence between patches. Moreover, we propose two evaluation metrics to measure the performance of appearance enhancement. The final experimental results have demonstrated the effectiveness of our learning-based enhancement method.

基于多尺度梯度域引导滤波的煤矿井下图像增强方法

煤矿井下图像存在较严重的光照不均匀和噪声干扰,现有基于Retinex的方法直接应用于煤矿井下图像增强易出现光晕伪影、边缘模糊、过增强和噪声放大等问题。针对上述问题,提出了一种基于多尺度梯度域引导滤波的煤矿井下图像增强方法。首先,将多尺度思想引入梯度域引导滤波中,实现对非均匀光照的准确估计,有效解决了增强图像时光晕伪影及边缘模糊的问题。然后,利用Retinex模型分离出光照分量和反射分量:对于光照分量,通过自适应伽马校正函数逐像素地修正光照信息,实现对图像暗区域增强的同时,抑制亮区域过增强,并使用限制对比度自适应直方图均衡化方法调整图像对比度;对于反射分量,将梯度域引导滤波与多尺度细节提升相结合,在准确去除噪声后提升纹理细节,避免了增强图像时噪声放大的问题。最后,将处理后的光照分量及反射分量融合,计算图像增益系数,并使用线性色彩恢复方法实现对原始RGB图像的逐像素增强,提升方法处理效率。实验结果表明,从主客观角度与现有方法相比,经所提方法处理后的图像在色彩保持、对比度、噪声抑制、细节保留等方面均取得了较好的增强效果,同时处理效率较高。

The digital mapping produced with satellite image of the Zhongshan Station area in Antarctica

Ice and snow domint the land features in Antarctica. The great brightness and poorcontrast of ice and snow and streaking noise in satellite image make the procedure of image processing difficult. On the other hand however, the contrast between bare rock land/sea water and ice/snow is so high that the details of image will be overcompressed.In the light of characteristics of satellite image in Antarctica, a filtering to remove streaking noise has adn discussed. Based on automatic identify classification to enhance the details of objects and the method and theory of digital rectification of satellite image with ground control points measured from field survey are also presented.