Weighted Least Squares Filter Research Articles

MdedFusion: A multi-level detail enhancement decomposition method for infrared and visible image fusion

To improve the infrared target features and preserve the texture details from source images, a simple and efficient multi-level detail enhancement decomposition method based on weighted least squares (WLS) and local statistical edge model (LSEM) is proposed, termed as MdedFusion. First, visibility enhancement of visible image is performed by using guided filter to avoid the effects of low-light environments. Then, infrared image and enhanced visible image are decomposed into a series of smooth parts and detail parts by WLS filter based multi-level decomposition mechanism. Next, in order to highlight edge features, a novel fusion strategy based on LSEM is proposed to make full use of variance difference for fusion of detail parts, and smooth parts are simply fused by weighted average (WAVG) strategy which is an efficient operation in our fusion framework. Finally, the fused image is obtained by reconstructing the fused smooth part and detail part. Specially, particle swarm optimization (PSO) algorithm is introduced to adaptively optimize the filter parameters in our framework. Compared with 16 state-of-the-art fusion methods, the experimental results show that the superiority of our proposed MdedFusion surpass the compared methods in highlighting infrared features and preserving texture detail information.

Naturalness balance contrast enhancement using adaptive gamma with cumulative histogram and median filtering

In the domain of contrast enhancement, contrast as well as detail retention are critical for human visualization and onward processing by any other computer based application. Contrast enhancement of an image with naturalness and preserved details is a challenge. In this paper, we propose a method using an edge-preserving smoothing operator approach, based on the weighted least squares (WLS) optimization. The detail and base layers are extracted using the WLS and median filters respectively. Base layer is a smooth layer of the image that contains large scale intensity changes and the detail layer captures the smaller scale details in the image. The layers are enhanced with a fine tuning between the rate of enhancement of the base and detail layers. It is vital to manage the level of enhancement in both the extracted layers as any mistune in the rate of enhancement may lead to severe distortion and addition of artifacts. Color correction is applied to the image to provide a higher-resolution improved image. The simulation results over 680 images from different datasets, show that the proposed method outperforms the existing methods in terms of qualitative and quantitative parameters.

Ship Detection in Multispectral Remote Sensing Images via Saliency Analysis

Despite the increasing visible optical remote sensing cameras equipped with panchromatic and four-band multispectral sensors, the application of multispectral data is still rarely used in the field of remote sensing ship target detection with the openly recognized challenge to improve detection precision. Towards this end, a ship target saliency detection method is proposed in the paper, which combines weighted least squares (WLS) with maximum symmetric surround (MSS), based on RGB-NIR four-band multispectral remote sensing images to locate the candidate area of ship targets quickly and accurately. The high frequency information of the NIR band image is extracted through the WLS filter and integrated into the RGB band image, and then the saliency of the image is analyzed. The detection results are combined with AIS to achieve complementary information for ship recognition. Some experiments show that the proposed method can effectively suppress the complex background information of clutter interference such as cloud waves and sea waves, highlight the ship target in low contrast scenes, and also increase recall and precision effectively.

A SAR Image Despeckling Method Based on an Extended Adaptive Wiener Filter and Extended Guided Filter

The elimination of multiplicative speckle noise is the main issue in synthetic aperture radar (SAR) images. In this study, a SAR image despeckling filter based on a proposed extended adaptive Wiener filter (EAWF), extended guided filter (EGF), and weighted least squares (WLS) filter is proposed. The proposed EAWF and EGF have been developed from the adaptive Wiener filter (AWF) and guided Filter (GF), respectively. The proposed EAWF can be applied to the SAR image, without the need for logarithmic transformation, considering the fact that the denoising performance of EAWF is better than AWF. The proposed EGF can remove the additive noise and preserve the edges’ information more efficiently than GF. First, the EAWF is applied to the input image. Then, a logarithmic transformation is applied to the resulting EAWF image in order to convert multiplicative noise into additive noise. Next, EGF is employed to remove the additive noise and preserve edge information. In order to remove unwanted spots on the image that is filtered by EGF, it is applied twice with different parameters. Finally, the WLS filter is applied in the homogeneous region. Results show that the proposed algorithm has a better performance in comparison with the other existing filters.

An RGB-NIR Image Fusion Method for Improving Feature Matching

The quality of RGB images can be degraded by poor weather or lighting conditions. Thus, to make computer vision techniques work correctly, images need to be enhanced first. This paper proposes an RGB image enhancement method for improving feature matching which is a core step in most computer vision techniques. The proposed method decomposes near-infrared (NIR) image into fine detail, medium detail, and base images by using weighted least squares filters (WLSF) and boosts the medium detail image. Then, the fine and boosted medium detail images are combined, and the combined NIR detail image replaces the luminance detail image of an RGB image. Experiments demonstrates that the proposed method can effectively enhance RGB image; hence more stable image features are extracted. In addition, the method can minimize the loss of the useful visual (or optical) information of the original RGB image that can be used for other vision tasks.

Hyperspectral Pansharpening Based on Intrinsic Image Decomposition and Weighted Least Squares Filter

Component substitution (CS) and multiresolution analysis (MRA) based methods have been adopted in hyperspectral pansharpening. The major contribution of this paper is a novel CS-MRA hybrid framework based on intrinsic image decomposition and weighted least squares filter. First, the panchromatic (P) image is sharpened by the Gaussian-Laplacian enhancement algorithm to enhance the spatial details, and the weighted least squares (WLS) filter is performed on the enhanced P image to extract the high-frequency information of the P image. Then, the MTF-based deblurring method is applied to the interpolated hyperspectral (HS) image, and the intrinsic image decomposition (IID) is adopted to decompose the deblurred interpolated HS image into the illumination and reflectance components. Finally, the detail map is generated by making a proper compromise between the high-frequency information of the P image and the spatial information preserved in the illumination component of the HS image. The detail map is further refined by the information ratio of different bands of the HS image and injected into the deblurred interpolated HS image. Experimental results indicate that the proposed method achieves better fusion results than several state-of-the-art hyperspectral pansharpening methods. This demonstrates that a combination of an IID technique and a WLS filter is an effective way for hyperspectral pansharpening.

A No-Reference Image Sharpness Metric Based on Large-Scale Structure

Most of no-reference image sharpness metrics suffer interference of tiny textures and the contrast around edges. Based on large-scale structure, this paper proposes a novel metric to avoid this problem. Firstly prominent edges are extracted by a weighted least-squares filter. Then, we calculate the statistics proportion of edges with different width and the mean value of the maximum gradients from the 51th to 250th as the features of the image. Finally, the support vector regression is used to get the relationship model between the features and the subjective assessment result. The metric can reduce the influence of textures, contrast and the content of images effectively, accordingly enhancing the assessment accuracy.

Robust centralized and weighted measurement fusion Kalman estimators for uncertain multisensor systems with linearly correlated white noises

This paper addresses the design of robust centralized fusion (CF) and weighted measurement fusion (WMF) Kalman estimators for a class of uncertain multisensor systems with linearly correlated white noises. The uncertainties of the systems include multiplicative noises, missing measurements, and uncertain noise variances. By introducing the fictitious noises, the considered system is converted into one with only uncertain noise variances. According to the minimax robust estimation principle, based on the worst-case system with the conservative upper bounds of uncertain noise variances, the robust CF and WMF time-varying Kalman estimators (predictor, filter, and smoother) are presented in a unified framework. Applying the Lyapunov equation approach, their robustness is proved in the sense that their actual estimation error variances are guaranteed to have the corresponding minimal upper bounds for all admissible uncertainties. Using the information filter, their equivalence is proved. Their accuracy relations are proved. The computational complexities of their algorithms are analyzed and compared. Compared with CF algorithm, the WMF algorithm can significantly reduce the computational burden when the number of sensors is larger. A robust weighted least squares (WLS) measurement fusion filter is also presented only based on the measurement equation, and it is proved that the robust accuracy of the robust CF or WMF Kalman filter is higher than that of robust WLS filter. The corresponding robust fused steady-state estimators are also presented, and the convergence in a realization between the time-varying and steady-state robust fused estimators is proved by the dynamic error system analysis (DESA) method. A simulation example shows the effectiveness and correctness of the proposed results.

An Adaptive Pansharpening Method by Using Weighted Least Squares Filter

Multisensor image fusion or pansharpening aims to sharpen a multispectral (MS) image by integrating the detail map derived from a panchromatic (Pan) image. The intensity–hue–saturation (IHS)-based methods are well adopted in pansharpening applications. However, the pansharpened MS images by IHS-based methods usually suffer from serious spectral distortions and local artifacts due to the mismatch between the estimated detail map and its ground truth. To overcome these defects, we propose a weighted least squares (WLS)-filter-based method in this letter. Different from existing IHS-based methods, the proposed method eliminates the influence of the low-frequency components of the Pan and MS images with the WLS filter. Moreover, the derived detail map is further refined based on the spectral signatures for different bands of the MS image. We test the proposed method on various satellites data; the experimental results demonstrate that the proposed method performs well in both spectral and spatial qualities.

Recognition improvement through optimized spatial support methodology

Spatial support is an effective method of improving object recognition that is widely used in the field of computer vision. Compared to various other spatial support methods, such as sliding windows, spatial support based on image segmentation is a classic technique with high-quality segmentation that can positively contribute to image recognition. However, over-segmentation and under-segmentation often occur in the segmentation process. It is difficult for object classifiers to recognize objects in low-quality, segmented images, thus low-quality segmentation will reduce recognition accuracy. In order to resolve this drawback, watershed segmentation, multi-scale decompositions based on WLS (Weighted-Least Squares) filters and multi-layer smoothing, have been used to process the images. This methodology was utilized to maintain the sharp regions' boundaries, while smoothing regions which could contribute to accurately segmenting the images. After obtaining high-quality segmentation, the images could then be utilized in image recognition. The superiority of this paper's methodology compared to that of previous methods has been demonstrated herein. Experiments using a large database have demonstrated that this methodology is capable of improving image recognition through optimizing segmentation results.

WLS filter for reducing atmospheric effects in spaceborne SAR tomography

A new approach was presented to eliminate the atmosphere-induced phase error utilizing only the single look complex (SLC) synthetic aperture radar (SAR) image set. This method exploited the space-invariance characteristic of phase error components contained in image pixels and estimates the phase error using the weighted least-squares (WLS) filter. Actually, this sort of method can be classified as autofocus algorithm which was generally applied in airborne SAR 2-D imaging to compensate the phase error introduced by airplane’s nonideal motion. Real data processing, which is relevant to Honda center and Angel stadium of Anaheim test-sites and acquired by Envisat-ASAR during the period from June 2004 to October 2007, was carried out to evaluate this WLS estimation algorithm. Experimental results show that the phase error estimated from WLS filter is very accurate and the focusing quality along NSR dimension is improved prominently via phase correction, which verifies the practicability of this new method.

Face Illumination Manipulation Using a Single Reference Image by Adaptive Layer Decomposition

This paper proposes a novel image-based framework to manipulate the illumination of human face through adaptive layer decomposition. According to our framework, only a single reference image, without any knowledge of the 3D geometry or material information of the input face, is needed. To transfer the illumination effects of a reference face image to a normal lighting face, we first decompose the lightness layers of the reference and the input images into large-scale and detail layers through weighted least squares (WLS) filter with adaptive smoothing parameters according to the gradient values of the face images. The large-scale layer of the reference image is filtered with the guidance of the input image by guided filter with adaptive smoothing parameters according to the face structures. The relit result is obtained by replacing the largescale layer of the input image with that of the reference image. To normalize the illumination effects of a non-normal lighting face (i.e., face delighting), we introduce similar reflectance prior to the layer decomposition stage by WLS filter, which make the normalized result less affected by the high contrast light and shadow effects of the input face. Through these two procedures, we can change the illumination effects of a non-normal lighting face by first normalizing the illumination and then transferring the illumination of another reference face to it. We acquire convincing relit results of both face relighting and delighting on numerous input and reference face images with various illumination effects and genders. Comparisons with previous papers show that our framework is less affected by geometry differences and can preserve better the identification structure and skin color of the input face.

Symmetric Structures for Odd-Order Maximally Flat and Weighted-Least-Squares Variable Fractional-Delay Filters

Our previous work has shown that coefficient symmetry can be exploited for designing high-accuracy and low-complexity even-order variable fractional-delay (VFD) filters. The objectives of this paper are twofold; One is to derive a coefficient symmetry for odd-order VFD filters, and then formulate a closed-form weighted-least-squares (WLS) design. Exploiting the coefficient symmetry along with different-order subfilters reduces the number of independent VFD filter coefficients by more than 50% , and thus reduces the hardware cost and the number of multipliers by more than 50%. Another objective is to briefly derive the maximally flat (MF) VFD filters from Nth-degree polynomial interpolation, and then present two kinds of transformation matrices for transforming causal odd-order MF VFD filters into symmetric structures such that the filter complexity can be reduced. As a result, both WLS and MF VFD filters can be implemented as the Farrow structure with significantly reduced complexity, which facilitates high-speed VFD filtering. Various examples are given to illustrate the effectiveness of the symmetry-based design and implementations.

Fast algorithms for identification of periodically varying systems

The problem of identification/tracking of periodically varying systems is considered. When system coefficients vary rapidly with time, the most frequently used weighted least squares (WLS) and least mean squares (LMS) algorithms are not capable of tracking the changes satisfactorily. To obtain good estimation results, one has to use more specialized adaptive filters, such as the basis function (BF) algorithms, which are based on explicit models of parameter changes. Unfortunately, estimators of this kind are numerically very demanding. The paper describes new recursive algorithms that combine low computational requirements, which are typical of WLS and LMS filters, with very good tracking capabilities, which are typical of BF filters.

Nonasymptotic results for finite-memory WLS filters

A nonasymptotic analysis of properties of weighted least squares (WLS) adaptive filters used for identification of time-varying systems is presented. It is shown that the problem of mean-square boundedness of WLS estimates is closely related to the problem of invertibility-in the mean sense-of the corresponding regression matrix. Necessary and sufficient conditions are discussed for such invertibility to hold. Based on that, a number of results are derived paralleling those already obtained for least mean-square (LMS) filters, and the problem of statistical robustness of the WLS estimator is briefly discussed. >