Fuzzy neural network image filter based on GA

A new nonlinear image filter using fuzzy neural network based on genetic algorithm is proposed. The learning of network parameters is performed by genetic algorithm with the efficient binary encoding scheme. In the following, fuzzy reasoning embedded in the network aims at restoring noisy pixels without degrading the quality of fine details. It is shown by experiments that the filter is very effective in removing impulse noise and significantly outperforms conventional filters.

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.

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.

An Image Filter Algorithm Using Level Set Based on Discontinuous PDE

An image filter based on nonlinear discontinuous partial differential equation （PDE） is presented. It models a class of morphological image filters called the level set method for gray image processing. We discuss the theoretical aspects of this PDE. The switch signal is controlled by the discontinuous right hand of PDE. We propose a discrete algorithm for its numerical solution and corresponding filter implementation. The study provides insights via several experiments. These types of filters are very useful in numerical image analyses.

Image denoising algorithm of refuge chamber by combining wavelet transform and bilateral filtering

In order to preferably identify infrared image of refuge chamber, reduce image noises of refuge chamber and retain more image details, we propose the method of combining two-dimensional discrete wavelet transform and bilateral denoising. First, the wavelet transform is adopted to decompose the image of refuge chamber, of which low frequency component remains unchanged. Then, three high-frequency components are treated by bilateral filtering, and the image is reconstructed. The result shows that the combination of bilateral filtering and wavelet transform for image denoising can better retain the details which are included in the image, while providing better visual effect. This is superior to using either bilateral filtering or wavelet transform alone. It is useful for perfecting emergency refuge system of coal mines.

Analysis on the Application of Image Processing Technology in Clothing Pattern Recognition

A clothing pattern is a significant embodiment of regional culture and national characteristics.The recognition of clothing patterns could be realized objectively and accurately by using digital image processing technology.The researches on the extraction techniques of various pattern elements were compared and analyzed.Then the researches on clothing pattern color,outline and fabric texture extraction were summarized.And the core technology chain model of clothing pattern extraction was constructed.The research status,the core technology and the development trend of pattern element extraction technology based on two-dimensional images were obtained.What’s more,a reference for the follow-up research of clothing patterns and the technology upgrading of textile and clothing industries were provided.

Quantum advantages for image filtering on images with efficient encoding and lower-bounded signal-to-noise ratio

Quantum image processing has long been a fascinating field,but establishing the existence of quantum speedup for all images remains challenging.In this study,we aim to identify a subset of images for which a quantum algorithm can be developed with a guaranteed advantage.Specifically,we present a quantum image filtering algorithm that exhibits an exponential speedup for efficiently encoded images with a lower-bounded signal-to-noise ratio.Our approach relies on a fixed-point Grover's search to emulate the effect of Hadamard multiplication with the filtering function.To demonstrate its effectiveness,we apply our method to three typical filtering problems.Additionally,we emphasize the significance of the efficient-encoding assumption by illustrating that the quantum speedup may diminish for images lacking efficient encoding.Our work underscores the importance of exploring image types and features to realize potential quantum advantages in image processing.

A noise removal algorithm for DR Images based on adaptive estimation of threshold

When DR （Digital Radiography） images are filtered, it is necessary to preserve the edges and key details. But the existing methods may inevitably take fine details mistaken for noise to remove. In order to solve the problem an improved anisotropic diffu- sion filtering model is proposed. Firstly, a novel diffusion function is introduced based on Perona and Malik model, which well overcomes the high rate of convergence. Secondly, the gradient threshold is modified to an adaptive estimation function, so it is bet- ter at adaptive threshold regulations according to the pixels and iteration times. Finally, the edges are extracted from the restored im- ages and the results are evaluated quantificationally. It is shown from the experiments that the proposed method is effective not only in noise reduction but also in details preserved.

A multi-scale scheme for image segmentation using neuro-fuzzy classification and curve evolution

In this paper, we present a new scheme to segment a given image. This scheme utilizes neuro-fuzzy system to derive a proper set of contour pixels based on multi-scale images. We use these fuzzy derivatives to develop a new curve evolution model. The model automatically detect smooth boundaries, scaling the energy term, and change of topology according to the extracted con- tour pixels set. We present the numerical implementation and the experimental results based on the semi-implicit method. Experi- mental results show that one can obtains a high clualitv edge contour.

Deep Root Memory Optimized Indexing Methodology for Image Search Engines

Digitization has created an abundance of new information sources by altering how pictures are captured.Accessing large image databases from a web portal requires an opted indexing structure instead of reducing the contents of different kinds of databases for quick processing.This approach paves a path toward the increase of efficient image retrieval techniques and numerous research in image indexing involving large image datasets.Image retrieval usually encounters difficulties like a)merging the diverse representations of images and their Indexing,b)the low-level visual characters and semantic characters associated with an image are indirectly proportional,and c)noisy and less accurate extraction of image information(semantic and predicted attributes).This work clearly focuses and takes the base of reverse engineering and de-normalizing concept by evaluating how data can be stored effectively.Thus,retrieval becomes straightforward and rapid.This research also deals with deep root indexing with a multidimensional approach about how images can be indexed and provides improved results in terms of good performance in query processing and the reduction of maintenance and storage cost.We focus on the schema design on a non-clustered index solution,especially cover queries.This schema provides a filter predication to make an index with a particular content of rows and an index table called filtered indexing.Finally,we include non-key columns in addition to the key columns.Experiments on two image data sets‘with and without’filtered indexing show low query cost.We compare efficiency as regards accuracy in mean average precision to measure the accuracy of retrieval with the developed coherent semantic indexing.The results show that retrieval by using deep root indexing is simple and fast.

Improved preprocessed Yaroslavsky filter based on shearlet features

An improved preprocessed Yaroslavsky filter（IPYF）is proposed to avoid the nick effects and obtain a better denoising result when the noise variance is unknown.Different from its predecessors,the similarity between two pixels is calculated by shearlet features.The feature vector consists of initial denoised results by the non-subsampled shearlet transform hard thresholding（NSST-HT）and NSST coefficients,which can help allocate the averaging weights more reasonably.With the correct estimated noise variance,the NSST-HT can provide good denoised results as the initial estimation and high-frequency coefficients contribute large weights to preserve textures.In case of the incorrect estimated noise variance,the low-frequency coefficients will mitigate the nick effect in cartoon regions greatly,making the IPYF more robust than the original PYF.Detailed experimental results show that the IPYF is a very competitive method based on a comprehensive consideration involving peak signal to noise ratio（PSNR）,computing time,visual quality and method noise.

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.

A gray-natural logarithm ratio bilateral filtering method for image processing

A new method based on gray-natural logarithm ratio bilateral filtering is presented for image smoothing in this work. A new gray-natural logarithm ratio range filter kernel, leading to adaptive magnitude from image gray distinction information, is pointed out for the bilateral filtering. The new method can not only well restrain noise but also keep much more weak edges and details of an image, and preserve the original color transition of color images. Experimental results show the effectiveness for image denoising with our method.

Hepatic CT image retrieval based on the combination of Gabor filters and support vector machine

Content-based image retrieval has been an active area of research for more than ten years. Gabor schemes and support vector machine （SVM） method have been proven effective in image representation and clas-sification. In this paper, we propose a retrieval scheme based on Gabor filters and SVMs for hepatic computed tomography （CT） images query. In our experiments, a batch of hepatic CT images containing several types of CT findings are used for the retrieval test. Precision comparison between our scheme and existing methods is presented.

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.

Reducing metal artifacts by restricting negative pixels

When the object contains metals,its x-ray computed tomography(CT)images are normally affected by streaking artifacts.These artifacts are mainly caused by the x-ray beam hardening effects,which deviate the measurements from their true values.One interesting observation of the metal artifacts is that certain regions of the metal artifacts often appear as negative pixel values.Our novel idea in this paper is to set up an objective function that restricts the negative pixel values in the image.We must point out that the naïve idea of setting the negative pixel values in the reconstructed image to zero does not give the same result.This paper proposes an iterative algorithm to optimize this objective function,and the unknowns are the metal affected projections.Once the metal affected projections are estimated,the filtered backprojection algorithm is used to reconstruct the final image.This paper applies the proposed algorithm to some airport bag CT scans.The bags all contain unknown metallic objects.The metal artifacts are effectively reduced by the proposed algorithm.

3D image reconstruction with controllable spatial filtering based on correlation of multiple periodic functions in computational integral imaging

We propose a novel method of slice image reconstruction with controllable spatial filtering by using the correlation of periodic delta-function arrays （PDFAs） with elemental images in computational integral imaging. The multiple PDFAs, whose spatial periods correspond to object＇s depths with the elemental image array （EIA）, can generate a set of spatially filtered EIAs for multiple object depths compared with the conventional method for the depth of a single object. We analyze a controllable spatial filtering effect by the proposed method. To show the feasibility of the proposed method, we carry out preliminary experiments for multiple objects and present the results.

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.

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.