Turbulence makes the image suffer from geometric distortion, pixel deviation and blur. This paper focuses on image restoration under atmospheric turbulence. To improve the image quality, we revisit the problem by a two-phase method. According to the distortion model analysis, we first combine affine transformation with non-rigid registration to suppress global motion and local pixel deviation. To improve the registration speed, the cost function is optimized by L-BFGS algorithm. Next, a multi-frame blind deconvolution algorithm is employed to restore the registered frames, and get a final output. The experimental results clearly demonstrate the effectiveness of the proposed method. It can effectively alleviate blur and distortions, improve visual quality and recovery speed significantly.
The visual quality assessment of images/videos is an ongoing hot research topic, which has become
more and more important for numerous image and video processing applications with the rapid development of digital
imaging and communication technologies. The goal of image quality assessment (IQA) algorithms is to automatically
assess the quality of images/videos in agreement with human quality judgments. Up to now, two kinds of models have
been used for IQA, namely full-reference (FR) and no-reference (NR) models. For FR models, IQA algorithms interpret
image quality as fidelity or similarity with a perfect image in some perceptual space. However, the reference image is not
available in many practical applications, and a NR IQA approach is desired. Considering natural vision as optimized by
the millions of years of evolutionary pressure, many methods attempt to achieve consistency in quality prediction by
modeling salient physiological and psychological features of the human visual system (HVS). To reach this goal,
researchers try to simulate HVS with image sparsity coding and supervised machine learning, which are two main
features of HVS. A typical HVS captures the scenes by sparsity coding, and uses experienced knowledge to apperceive
objects. In this paper, we propose a novel IQA approach based on visual perception. Firstly, a standard model of HVS is
studied and analyzed, and the sparse representation of image is accomplished with the model; and then, the mapping
correlation between sparse codes and subjective quality scores is trained with the regression technique of least squaresupport
vector machine (LS-SVM), which gains the regressor that can predict the image quality; the visual metric of
image is predicted with the trained regressor at last. We validate the performance of proposed approach on Laboratory
for Image and Video Engineering (LIVE) database, the specific contents of the type of distortions present in the database
are: 227 images of JPEG2000, 233 images of JPEG, 174 images of White Noise, 174 images of Gaussian Blur, 174
images of Fast Fading. The database includes subjective differential mean opinion score (DMOS) for each image. The
experimental results show that the proposed approach not only can assess many kinds of distorted images quality, but
also exhibits a superior accuracy and monotonicity.
The goal of image restoration is to reconstruct the original scene from a degraded observation. It is a critical and
challenging task in image processing. Classical restorations require explicit knowledge of the point spread function and a
description of the noise as priors. However, it is not practical for many real image processing. The recovery processing
needs to be a blind image restoration scenario. Since blind deconvolution is an ill-posed problem, many blind restoration
methods need to make additional assumptions to construct restrictions. Due to the differences of PSF and noise energy,
blurring images can be quite different. It is difficult to achieve a good balance between proper assumption and high
restoration quality in blind deconvolution. Recently, machine learning techniques have been applied to blind image
restoration. The least square support vector regression (LSSVR) has been proven to offer strong potential in estimating
and forecasting issues. Therefore, this paper proposes a LSSVR-based image restoration method. However, selecting the
optimal parameters for support vector machine is essential to the training result. As a novel meta-heuristic algorithm, the
fruit fly optimization algorithm (FOA) can be used to handle optimization problems, and has the advantages of fast
convergence to the global optimal solution. In the proposed method, the training samples are created from a
neighborhood in the degraded image to the central pixel in the original image. The mapping between the degraded image
and the original image is learned by training LSSVR. The two parameters of LSSVR are optimized though FOA. The
fitness function of FOA is calculated by the restoration error function. With the acquired mapping, the degraded image
can be recovered. Experimental results show the proposed method can obtain satisfactory restoration effect. Compared
with BP neural network regression, SVR method and Lucy-Richardson algorithm, it speeds up the restoration rate and
performs better. Both objective and subjective restoration performances are studied in the comparison experiments.
This paper proposes an automatic method for IR and visible images matching without any assumption about initial
alignment. This paper details our interest region extraction method for optical images and also the efficient region
matching component. An improved shape context descriptor is constituted. The algorithm introduces in uniform pattern
to make the extracted component decrease to 20 with rotation invariance Experiments using several IR and visible
images illustrate the effectiveness of the proposed even when facing considerably geometric distortions. Even at different
time and under special weather condition, it still has a higher average correct matching ratio than SURF and better
robustness and it has a high efficiency, a short running time.
In order to effective defend DDoS attacks in network confrontation, an active defense scheme against DDoS is built based on Mobile Agent and network control. A distributed collaborative active defense model is constructed by using mobile agent technology and encapsulating a variety of DDoS defense techniques. Meanwhile the network control theory is applied to establish a network confrontation’s control model for DDoS to control the active defense process. It provides a new idea to solve the DDoS problem.
The concept of invariant moment of gradient orientation histogram and a novel line matching algorithm based on the
invariant moment of histogram are proposed to resolve the problem of matching typical objects in the IR and visible images.
First, line segments are extracted. Second, the average gradient vector direction of each pixel on the line is adopt as the main
direction of the line. Third, the line is divided into non-overlapped sub-regions with the same size. The gradient vector
direction of each sub-region are constructed, and the weighted invariant moment of histogram in each sub-region are
calculated to build the line descriptor. Finally, the feature matching is realized via the NNDR(nearest/next ratio) method.
Experimental results show that the proposed algorithm can match the typical objects in the IR and visible images efficiently
Multiresolution-based image fusion has been the focus of considerable research attention in recent years with a number
of algorithms proposed. In most of the algorithms, however, the parameter configuration is usually based on experience.
This paper proposes an adaptive image fusion algorithm based on the nonsubsampled contourlet transform (NSCT),
which realizes automatic parameter adjustment and gets rid of the adverse effect caused by artificial factors. The
algorithm incorporates the quality metric of structural similarity (SSIM) into the NSCT fusion framework. The SSIM
value is calculated to assess the fused image quality, and then it is fed back to the fusion algorithm to achieve a better
fusion by directing parameters (level of decomposition and flag of decomposition direction) adjustment. Based on the
cross entropy, the local cross entropy (LCE) is constructed and used to determine an optimal choice of information
source for the fused coefficients at each scale and direction. Experimental results show that the proposed method
achieves the best fusion compared to three other methods judged on both the objective metrics and visual inspection and
exhibits robust against varying noises.
Based on the nonsubsampled contourlet transform (NSCT) and two denoising models (i.e., fractional power model and
cross-scale correlation model), an efficient pre-processing algorithm for infrared image is proposed. In our algorithm, the
NSCT is used to decompose the image at different scale and orientation, and then implement pre-processing in the
frequency domain, at last reconstruct coefficients to obtain ideal infrared image. The key of the proposed algorithm is
pre-processing which includes noise removal and information enhancement. To reduce the two kinds of noises (i.e.,
Gaussian noise and shot noise) efficiently, the two models referred are applied to the NSCT coefficients respectively.
The filtered results are fused to learn from the strong points of each denoising methods to offset the weakness of each
other. Later, the denoised coefficients are classified to edges and noise and modified by a nonlinear mapping function.
Experiments carried on infrared images show that the new algorithm can reduce the Gaussian noise and shot noise
efficiently, while keeping the detail information well. Both in the objective performance index and subjective viewing
assessment, the new algorithm is superior to the DWT-based method as well as the traditional method.