Images often suffer from low visibility under nonuniform illumination, weak luminance and backlight environment. This paper describes a novel approach to improvement the visualization of poor light conditions. Firstly, we raise the global brightness using an adaptive exponent induced function. To enhance the local detail perception, the local contrast is boosted by contrast preserving which utilizes human vision system model. To not bias from original image, we generate the contrast combined original image and global illuminance enhance output in the gradient domain. To reduce artifacts, the guided filter is employed to estimate the local mean illuminance when transform the contrast. The experimental results demonstrate that our proposed method has a pleasant visual effect and low computational complexity than the state of the arts.
In this paper, we introduce a novel nonuniformity correction (NUC) algorithm for infrared focal-plane array (IRFPA). It is based on layers technique. First, the Rolling Guidance Filter (RGF) is utilized to decompose the raw IR image into a low frequency part and a high frequency part. Then, an adaptive temporal high-pass filter is utilized to filter the high frequency part by making use of the gradient and amplitude of it to estimate the Fixed Pattern Noise (FPN). The proposed scheme use the frames with large displacement to estimate the FPN to alleviate the ghosting artifacts in case of scene moves slowly. At Last, the estimated FPN is subtracted from the pristine image to obtain the correct result. Experiments with synthetic and real IR video demonstrate that the proposed method has better NUC performance and less artifacts than the state-of-the-arts.
Fixed pattern noise (FPN) in infrared images seriously degrades the imaging quality and visual effect of infrared focal plane arrays (FPAs). Although many scene-based non-uniformity correction (NUC) algorithms have been developed recent years, the convergence speed of the bias and gain correction parameters still need to be further improved. In this paper, we present a novel NUC approach for IR FPAs which minimizes the total variation of the estimated IR irradiance guided by a noise model image, and we name it guided total variation (GTV) NUC method. A temporal detection factor is introduced to NUC procedure to prevent NU parameters updating when scene movement stops. In the proposed scheme, the correction parameters of the FPN are estimated via an iterative optimization strategy, frame by frame. The experimental results of synthetic and real IR videos demonstrate that the proposed algorithm have better NUC performance in terms of fewer ghosting artifacts and faster convergence than the state-of-the-art methods.
KEYWORDS: Sensors, Monte Carlo methods, Information fusion, Computer simulations, Error analysis, Information technology, Data processing, Intelligent sensors, Sensor fusion, Optimization (mathematics)
In this paper we propose an independent sequential maximum likelihood approach to address the joint track-to-track association and bias removal in multi-sensor information fusion systems. First, we enumerate all kinds of association situation following by estimating a bias for each association. Then we calculate the likelihood of each association after bias compensated. Finally we choose the maximum likelihood of all association situations as the association result and the corresponding bias estimation is the registration result. Considering the high false alarm and interference, we adopt the independent sequential association to calculate the likelihood. Simulation results show that our proposed method can give out the right association results and it can estimate the bias precisely simultaneously for small number of targets in multi-sensor fusion system.
Small target detection in the clutter infrared image is a tough but significant work. In this paper, we will propose a novel
small target detection method. First, Graph Laplacian regularization is utilized to model similarity feature of graph
structure in the image. And Graph Laplacian regularization is incorporated in the background estimation model to
preserve edges of background in single frame infrared image. At last, the edge-preserving estimated background is
eliminated from original image to get foreground image which is used to detect the small target. Experimental results
show that our proposed method can achieve edge-preserving estimation of background, suppress clutter efficiently, and
get better detection results.
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