Local Contrast Measure Research Articles

Infrared Small Target Detection Based on Weighted Improved Double Local Contrast Measure

The robust detection of infrared small targets plays an important role in infrared early warning systems. However, the high-brightness interference present in the background makes it challenging. To solve this problem, we propose a weighted improved double local contrast measure (WIDLCM) algorithm in this paper. Firstly, we utilize a fixed-scale three-layer window to compute the double neighborhood gray difference to screen candidate target pixels and estimate the target size. Then, according to the size information of each candidate target pixel, an improved double local contrast measure (IDLCM) based on the gray difference is designed to enhance the target and suppress the background. Next, considering the structural characteristics of the target edge, we propose the variance-based weighting coefficient to eliminate clutter further. Finally, the targets are detected by an adaptive threshold. Extensive experimental results demonstrate that our method outperforms several state-of-the-art methods.

Watermarking Algorithm for Remote Sensing Images based on Ring-Shaped Template Watermark and Multiscale LCM

Identifying template watermarks under severe geometric distortions is a significant scientific problem in the current watermarking research for remote sensing images. We propose a novel watermarking algorithm that integrates the ring-shaped template watermark with the multiscale local contrast measure (LCM) method. In the embedding stage, the ring-shaped template watermark is embedded into the discrete Fourier transform (DFT) magnitude coefficients, converting the watermark into small targets in the DFT domain. During the detection stage, the multiscale LCM, a classic infrared small target detection method, enhances these small targets and generates a contrast map. Peak detection is then performed on the contrast map to determine the radius of the template watermark. Finally, circular edge local binarization is applied to extract the watermark information. The proposed method enables synchronization recovery of watermarks under blind conditions. The experimental results demonstrate that the method possesses strong robustness against various geometric attacks such as rotation, scaling, translation, and cropping. It outperforms comparative algorithms in terms of robustness and also exhibits good imperceptibility.

Infrared small target detection based on variance difference weighted three-layer local contrast measure

Infrared (IR) small target detection technology is of paramount importance in the infrared search and track (IRST) system. The current methods typically exhibit strong performance in simple backgrounds, but they struggle to effectively suppress high-brightness background and strong noise. For the sake of diminish the false alarm rate of infrared small target detection algorithms under strong clutter interference, an infrared small target detection algorithm based on variance difference weighted three-layer local contrast measure (VDWTLCM) is proposed in this paper. The algorithm comprises two modules: the three-layer local contrast measure (TLCM) and the weighting function based on mean value of the variance difference (MVD). In the calculation module of TLCM, the image is processed pixel by pixel using a three-layer sliding window, while both the ratio form definition and the difference form definition are utilized to calculate the local contrast for each pixel, which can enhance the target and eliminate the high-brightness background. To calculate the MVD weighting function, a modified layered gradient kernel is first introduced to pre-process the IR image, aimed at achieving more effective clutter smoothing. And then the concept of variance difference is employed to further suppress the highlighted background edges. Ultimately, the target is extracted utilizing adaptive threshold segmentation. The experimental results demonstrate that, compared with the common infrared small target detection algorithms, the proposed algorithm improves the signal to clutter ratio gain (SCRG) by an average of 13.13 times and the background suppression factor (BSF) by an average of 10.02 times, and can achieve superior detection performance in different complex scenarios.

Infrared Small Target Detection Based on Local Contrast Measure With a Flexible Window

Infrared Small Target Detection Based on Local Contrast Measure With a Flexible Window

Label Evolution Based on Local Contrast Measure for Single-Point Supervised Infrared Small-Target Detection

Label Evolution Based on Local Contrast Measure for Single-Point Supervised Infrared Small-Target Detection

Bisection Window and Homogeneity Principle- Based Local Contrast Measure for Infrared Sea–Sky Line Detection

Bisection Window and Homogeneity Principle- Based Local Contrast Measure for Infrared Sea–Sky Line Detection

Infrared small target detection based on ring local contrast measure with edge suppression

Infrared small target detection based on ring local contrast measure with edge suppression

Using Double-Layer Patch-Based Contrast for Infrared Small Target Detection

Detecting infrared (IR) small targets effectively and robustly is crucial for the tasks such as infrared searching and guarding. While methods based on the human vision system (HVS) have achieved great success in this field, detecting dim targets in complex backgrounds remains a challenge due to the multi-scale framework and over-simplified disparity calculations. In this paper, infrared small targets are detected with a novel local contrast measurement named double-layer patch-based contrast (DLPC). Firstly, we crafted an elaborated double-layer local contrast measure, to suppress the background, which can accurately measure the gray difference between the target and its surrounding complex background. Secondly, we calculated the absolute value of the grayscale difference between the target and the background in the diagonal directions as a weighting factor to further enhance the target. Then, an adaptive threshold on the DLPC was employed to extract the target from the IR image. The proposed method can detect small targets effectively with a fixed-scaled mask template while being computationally efficient. Experimental results in terms of background suppression factor (BSF), signal-to-clutter ratio gain (SCRG) and receiver operating characteristic (ROC) curve on five IR image datasets demonstrated that the proposed method has better detection performance compared to six state-of-the-art methods and is more robust in addressing complex backgrounds.

Detection of infrared small target based on background subtraction local contrast measure and Gaussian structural similarity

Infrared (IR) small target detection, especially in a complex background, continues to present challenges in the low false alarm rate and high robustness. In this paper, a background subtraction local contrast measure (BSLCM) and Gaussian structural similarity (GSS) integrated structural model is proposed to detect IR small target. In the proposed model, BSLCM is used to suppress the complex background and enhance the target. GSS calculation is conducted to eliminate the high-brightened background residual and noise further. To evaluate the performance of the proposed method, real IR sequences and seven state-of-the-art (SOTA) methods were adopted. The results demonstrated that the BSLCM can suppress all types of strong background clutter and enhance the true target effectively.

Infrared Small Target Detection via Center-Surround Gray Difference Measure With Local Image Block Analysis

Exristing algorithms may suffer from high false alarm rate and low detection probability when detecting dim small target under intricate background clutters and heavy noise. To address this problem, a target detection method based on local image block analysis and center-surround gray difference measure (CGDM) is proposed in this article. First, the infrared image is decomposed into a group of local blocks. The gray information of each block are sorted in ascending order and the last element is deleted, then the maximum gray jump point (MGJP) is extracted by using differential operation. Second, a protection zone is formed with MGJP as the center, and the maximal intensity of protection zone (MIPZ) is extracted. Next, by comparing the gray intensity of MGJP and MIPZ values, the potential target blocks can be reliably selected, while most background blocks and noise blocks will be discarded. After that, the CGDM is presented to enhance the target and suppress background clutters, and then, the center-surround local contrast measure (CLCM) is designed to further suppress the high-intensity clutter residues by searching the target center and revising the protection zone. Finally, the weighted center-surround gray difference measure (WCGDM) is defined by CLCM WCGDM map to recognize real targets. Extensive experiments show that the proposed method outperforms several existing algorithms in small target detection under complex background clutters, and it is robust to various target shapes, target sizes, and noise types.

Infrared Small-Target Detection Based on Multi-level Local Contrast Measure

Infrared small target detection technology is one of the key technologies for reconnaissance, guidance, and early warning systems, and it has important theoretical and practical value to conduct in-depth research on it. However, there are several challenges in infrared small target detection. Firstly, infrared small targets have low signal-to-noise ratio, which makes them easily submerged in complex backgrounds. Secondly, since infrared small target detection is a long-distance imaging process, there is no shape or texture information available, which increases the difficulty of target detection. To address these challenges, this paper proposes a multi-level contrast enhancement method to suppress structural background, and develops a more effective detection algorithm. Based on the concept of local contrast measurement (LCM), a new contrast-based small target detection algorithm called Multi-Level Local Contrast Measurement (MLLCM) is constructed, and its effective implementation process is provided. Compared with LCM, MPCM (Multiscale Patch-based Contrast Measure), and other algorithms, this algorithm effectively enhances the target area and eliminates background clutter. The results on simulated images demonstrate the effectiveness of this algorithm.

High-boost-based local Weber contrast method for infrared small target detection

ABSTRACT High-precision detection of small targets with low signal-to-noise ratio in infrared (IR) images has great significance for IR precise guidance, video surveillance, etc. In this paper, we proposed a novel method called centre-surround high-boost filter-based improved local Weber contrast measure. First, centre-surround high-boost filter is proposed to fully utilize the grey difference information, which reduces the influence of the non-homogeneous background such as cumulus sky, sea-sky clutters and high brightness edge. Then, an improved local Weber contrast measure is used to construct the contrast operator of the ratio-difference joint form for further target contrast enhancement and arbitrary interference suppression. Finally, we apply an adaptive thresholding segmentation to find the true target. Experimental results on various cluttered background sequences demonstrate that our proposed method can detect small targets with higher detection rate and lower false alarm rate, and the performance is more stable than five comparable methods in diverse scenarios.

Infrared Small Dim Target Detection Under Maritime Near Sea–Sky Line Based on Regional-Division Local Contrast Measure

Infrared Small Dim Target Detection Under Maritime Near Sea–Sky Line Based on Regional-Division Local Contrast Measure

Infrared small target detection based on joint local contrast measures

Fast and accurate detection of dim and small targets is a key feature in infrared (IR) search and tracking systems. Small targets with no obvious features are usually submerged in complex backgrounds and clutter, causing low detection rates for most methods. In this paper, a novel joint constraint local contrast measure algorithm is proposed to detect small IR targets. It consists of two modules. First, we define a ratio-difference measure to enhance the small target and suppress the background. Second, a constrained difference measure is defined to suppress clutter and enhance the target. The two contrast measures are combined to obtain the saliency map. Finally, an adaptive threshold is calculated to extract the target. Experiments on a series of real IR images and sequences demonstrate that the proposed method can achieve better detection performance than other state-of-the-art methods.

Small and dim infrared moving target detection based on spatial–temporal saliency

Infrared target detection is a significant work in military, high accuracy and high speed are the essential capabilities for precise guidance. With limited conditions, making full use of spatial–temporal information can really improve performance. This article studied small and dim infrared moving target detection based on sequence images. Quaternion Fourier Transform is an efficient method of visual saliency. To make this algorithm more accurate, we introduced four preprocessing feature map as input channels for quaternion. They are selective inter frame difference, fuzzy-based morphology filter, joint local gradient and gray contrast measure and temporal-constrained Gaussian curvature filter. All of them aim to highlight small-dim target and help to construct saliency map. Simultaneously, a trajectory constraint is introduced to reduce false alarm rate. Compared with single-frame based methods and multi-frames based methods, our novel algorithm performed better on balancing time consuming and detection precision.

Mineral Texture Identification Using Local Binary Patterns Equipped with a Classification and Recognition Updating System (CARUS)

In this paper, a rotation-invariant local binary pattern operator equipped with a local contrast measure (riLBPc) is employed to characterize the type of mineral twinning by inspecting the texture properties of crystals. The proposed method uses photomicrographs of minerals and produces LBP histograms, which might be compared with those included in a predefined database using the Kullback–Leibler divergence-based metric. The paper proposes a new LBP-based scheme for concurrent classification and recognition tasks, followed by a novel online updating routine to enhance the locally developed mineral LBP database. The discriminatory power of the proposed Classification and Recognition Updating System (CARUS) for texture identification scheme is verified for plagioclase, orthoclase, microcline, and quartz minerals with sensitivity (TPR) near 99.9%, 87%, 99.9%, and 96%, and accuracy (ACC) equal to about 99%, 97%, 99%, and 99%, respectively. According to the results, the introduced CARUS system is a promising approach that can be applied in a variety of different fields dealing with classification and feature recognition tasks.

Automatic segmentation of hyperreflective dots via focal priors and visual saliency.

Hyperreflective dots (HRDs) can be observed in spectral domain optical coherence tomography (SD-OCT), which can provide a sensitive marker in the treatment decision process. Quantitative analyses of HRDs are the key to make appropriate decisions on observation, treatment, and retreatment. The purpose of this study is to automatically and accurately segment HRDs in SD-OCT B-scans with diabetic retinopathy (DR). The authors propose an automatic segmentation algorithm of HRDs via focal priors and visual saliency. The algorithm is divided into three stages: segmentation of retinal layers, calculation of the multiscale local contrast saliency map, and adaptive threshold segmentation. First, a method based on improved graph search is used to segment retinal layers to obtain the region of interest (ROI) and the reflectivity estimation of the retinal pigment epithelium (RPE) layer; then, the multiscale local contrast saliency map is obtained by using a local contrast measure, which measures the dissimilarity between the current pixels and corresponding neighborhoods; finally, an adaptive threshold is applied to segment HRDs. Experimental results on 20 SD-OCT B-scans demonstrate that our method is effective for HRDs segmentation. The average dice similarity coefficient (DSC) and detection accuracy are 71.12% and 85.07%, respectively. The proposed method can accurately segment HRDs in SD-OCT B-scans with DR and outperforms current state-of-the-art methods. Our method can provide reliable HRDs segmentation to assist ophthalmologists in clinical diagnosis, treatment, disease monitoring, andprogression.

Visible and Infrared Image Fusion-Based Image Quality Enhancement with Applications to Space Debris On-Orbit Surveillance

The increasing amount of space debris in recent years has greatly threatened space operation. In order to ensure the safety level of spacecraft, space debris perception via on-orbit visual sensors has become a promising solution. However, the perception capability of visual sensors largely depends on illumination, which tends to be insufficient in dark environments. Since the images captured by visible and infrared sensors are highly complementary in dark environments, a convolutional sparse representation-based visible and infrared image fusion algorithm is proposed in this paper to expand the applicability of visual sensors. In particular, the local contrast measure is applied to obtain the refined weight map for fusing the base layers, which is more robust in a dark space environment. The algorithm can settle two significant problems in space debris surveillance, namely, improving the signal-noise ratio in a noise space environment and preserving more detailed information in a dark space environment. A space debris dataset containing registered visible and infrared images has been purposely created and used for algorithm evaluation. Experimental results demonstrate that the proposed method in this paper is effective for enhancing image qualities and can achieve favorable effects compared to other state-of-the-art algorithms.

A Pan-Sharpening Method with Beta-Divergence Non-Negative Matrix Factorization in Non-Subsampled Shear Transform Domain

In order to combine the spectral information of the multispectral (MS) image and the spatial information of the panchromatic (PAN) image, a pan-sharpening method based on β-divergence Non-negative Matrix Factorization (NMF) in the Non-Subsampled Shearlet Transform (NSST) domain is proposed. Firstly, we improve the traditional contrast calculation method to build the weighted local contrast measure (WLCM) method. Each band of the MS image is fused by a WLCM-based adaptive weighted averaging rule to obtain the intensity component I. Secondly, an image matting model is introduced to retain the spectral information of the MS image. I is used as the initial α channel to estimate the foreground color F and the background color B. Depending on the NSST, the PAN image and I are decomposed into one low-frequency component and several high-frequency components, respectively. Fusion rules are designed corresponding to the characteristics of the low-frequency and high-frequency components. A β-divergence NMF method based on the Alternating Direction Method of Multipliers (ADMM) is used to fuse the low frequency components. A WLCM-based rule is used to fuse the high-frequency components. The fused components are inverted by NSST inverse transformation, and the obtained image is used as the final α channel. Finally, the final fused image is reconstructed according to the foreground color F, background color B, and the final α channel. The experimental results demonstrate that the proposed method achieves superior performance in both subjective visual effects and objective evaluation, and effectively preserves spectral information while improving spatial resolution.

Infrared Small-Target Detection Using Multiscale Local Average Gray Difference Measure

In infrared (IR) guidance and target tracking systems, dim target intensity and complex background clutter are some of the typical challenges, especially for the accurate detection of small objects. In this article, we propose a novel IR target detection method based on new local contrast measures. First, the local average gray difference measure (LAGDM) is presented to accentuate the difference between a small object and its local background. Then, an LAGDM map is generated to effectively enhance targets and suppress background clutter. Finally, we use an adaptive segmentation method to separate the object from the background. Experimental results on multiple sequences show that the proposed small-target detection method can effectively improve the signal-to-clutter ratio (SCR) of the image, and it exhibits robust performance against cloudy sky, sea sky, and mountain forest backgrounds.