Supatana Auethavekiat

A fast image recovery using compressive sensing technique with block based Orthogonal Matching Pursuit

Traditionally, the problems of applying Orthogonal Matching Pursuit (OMP) to large images are its high computing time and its requirement for a large matrix. In this paper, we propose a fast image recovery algorithm by dividing the image into block of n×n pixels and applying OMP to each n×n block instead of the entire image. The key idea is that small matrix requires less computing time and less memory. In the experiment, the block based OMP was applied to three standard test images: Lena, Mandrill and Pirate. Compared to standard OMP, block based OMP required less computing time while giving comparable PSNR.

Robust reconstruction algorithm for compressed sensing in Gaussian noise environment using orthogonal matching pursuit with partially known support and random subsampling

The compressed signal in compressed sensing (CS) may be corrupted by noise during transmission. The effect of Gaussian noise can be reduced by averaging, hence a robust reconstruction method using compressed signal ensemble from one compressed signal is proposed. The compressed signal is subsampled for L times to create the ensemble of L compressed signals. Orthogonal matching pursuit with partially known support (OMP-PKS) is applied to each signal in the ensemble to reconstruct L noisy outputs. The L noisy outputs are then averaged for denoising. The proposed method in this article is designed for CS reconstruction of image signal. The performance of our proposed method was compared with basis pursuit denoising, Lorentzian-based iterative hard thresholding, OMP-PKS and distributed compressed sensing using simultaneously orthogonal matching pursuit. The experimental results of 42 standard test images showed that our proposed method yielded higher peak signal-to-noise ratio at low measurement rate and better visual quality in all cases.

Impulsive noise rejection method for compressed measurement signal in compressed sensing

The Lorentzian norm of robust statistics is often applied in the reconstruction of the sparse signal from a compressed measurement signal in an impulsive noise environment. The optimization of the robust statistic function is iterative and usually requires complex parameter adjustments. In this article, the impulsive noise rejection for the compressed measurement signal with the design for image reconstruction is proposed. It is used as the preprocessing for any compressed sensing reconstruction given that the sparsified version of the signal is obtained by utilizing octave-tree discrete wavelet transform with db8 as the mother wavelet. The presence of impulsive noise is detected from the energy distribution of the reconstructed sparse signal. After the noise removal, the noise corrupted coefficients are estimated. The proposed method requires neither complex optimization nor complex parameter adjustments. The performance of the proposed method was evaluated on 60 images. The experimental results indicated that the proposed method effectively rejected the impulsive noise. Furthermore, at the same impulsive noise corruption level, the reconstruction with the proposed method as the preprocessing required much lower measurement rate than the model-based Lorentzian iterative hard thresholding.

Foreground rejection for parallax removal in video sequence stitching

In image stitching, parallax is key challenge that leads to inaccurate registration and ghosting effect of objects in the result. A novel foreground rejection method is proposed in this paper to remove parallax in video sequence. Firstly, the global motion is estimated between two frames using SIFT feature matching. The logical OR operators are then applied to the pixels that have high displaced frame difference for foreground extraction. There are two sets of foreground calculated from the near frames and far frames. Voting scheme is applied in the way that only the near-frame foreground appears inside the area of far-frame foreground that is considered as the actual foreground. The extracted foreground at this stage is mostly the edges of objects. Therefore, the foreground rejection method proposed in this paper aims to provide better motion vector estimation and ghosting effect reduction. The removal of the major parts of the high difference foreground is sufficient; hence the simple two-stage rendering is applied. The implemented results indicate that more accurate registration is achieved by including our proposed foreground rejection method. Moreover, the background of the stitched video sequence does not have the visible ghosting effect.

Approximation method for interpolating affine parameters in doubling frame rate

The quality of the frame rate up-conversion depends on the accuracy of the motion parameters. 2D affine model describes the motion better than the popular 2D translation model; however, its parameters are difficult to interpolate. In this paper, the approximation method for interpolating the mid-point of 2D affine parameters is proposed. The affine transform is approximated as the 2D rigid transformation where the target x and y axes are not orthogonal. Trigonometry relationship is applied to interpolate rotational parameters. Conventional linear interpolation is applied to interpolate translational parameters. For video sequences containing complex motion, the reconstruction from the approximated affine parameters gave higher PSNR and visually better result than the one from the interpolated 2D translation parameters.

Directional local mean difference level set method for locating a urinary bladder lumen in brachytherapy

In this paper, we propose the directional local mean difference level set method (DLMD-LS). Our work is focused on the segmentation for a urinary bladder lumen in a T2 weighted image taken during brachytherapy. The boundary is detected as the region where the intensity means of the areas inside and outside the zero-level contour (edge) are high. The contour is controlled such that it stops evolving only at the boundary of the bright object inside the dark surrounding. Since the intensity mean is used, the DLMD-LS is more tolerant to noise than the conventional level set methods which detect the boundary as the part with large intensity gradient. Furthermore, the mean is calculated locally; therefore, the function is less affected to the different intensity distribution inside the lumen. The experiment demonstrates the superior noise tolerance and better capability to capture the blurred boundary of our proposed DLMD-LS to the directional level set method and the level set which detects the boundary according to the intensity distribution of the entire lumen.

High frequency preserving fast compressive sensing based on wavelet block orthogonal matching pursuit

Due to large data set, block processing is usually applied for fast compressive sensing (CS) reconstruction; however, it gives the undesired blocking artifact in reconstructed data. In order to reduce blocking artifact and preserve high frequency, this paper proposes a novel block processing on wavelet domain instead of spatial domain. No post-processing nor special mapping is included. CS is applied only on blocks where the data are really sparse. An enhancing process, often included for artifact reduction, is no longer necessary. Our algorithm was evaluated by reconstructing three standard images (Lena, Mandrill and Peppers) and then compared with scrambled block hadamard ensemble (SBHE) and block-based CS sampling with a smoothed PL variant using directional discrete wavelet transform (BCS-SPL-DDWT). In the experiment, it provided better reconstruction both objectively (PSNR) and subjectively at low measurement rate. It gave the sharpest image in all cases. Details were preserved and blocking artifact was not detectable.

Directional Local Mean Difference Level Set method with Reinforcement Learning

Directional Local Mean Difference Level Set method (DLMD-LS) is the segmentation method for a urinary bladder in an MR sequence used for planning the treatment of a cervical cancer by radiation. The blurred boundary of a bladder is segmented based on the judgment of a radiologist and can be differed among radiologists. In this paper, DLMD-LS with Reinforcement Learning (RL) is proposed. It is an interactive system, where the parameter is adjusted to reflect the individual judgment. The weighted average method is used to update the distance that the boundary will be expanded, after the level set contour finishes evolving. The experiment on 30 MR slices demonstrated that DLMD-LS with RL had high segmentation accuracy and was adaptable to the new radiologist. It was also robust to outliers.

L 1 norm of high frequency components as a regularization term for compressed sensing reconstruction of image signals

This paper proposes a formulation of estimating a sparse image signal where the sparsity only occurs on the high frequency components. The formulation is for compressed sensing reconstruction when a compressed measurement signal is contaminated by impulsive noise. The approach is based on two prior well-known assumptions. First, adding an L1-norm penalty on a compressible signal to the cost objective of the estimation problem will promote a sparsity in the signal. Secondly, an image signal should be sparse in high frequency domain, while it is dense in low frequency domain where most of its important information lies. Our approach is therefore to impose an L1-norm penalty only on the signal components that need to be sparse while we allow the important low frequency part to be dense. The obtained formulation is a convex optimization which can be effectively solved by many available algorithms. The experiment demonstrated a superior impulsive noise tolerance of the proposed penalty function to a conventional scheme where all signal components are penalized by L1-norm. At the noise probability of less than 0.1, the reconstruction was better both quantitatively and qualitatively.

Greedy steep slope finder: The fast impulsive noise rejection for compressed measurement image signals

Impulsive noise in a compressed measurement signal can be detected by the energy distribution of the reconstructed sparse signal. In this paper, the fast preprocessing for impulsive noise rejection for a compressed measurement image signal is proposed. An image is sparsified by octave-tree wavelet transform with db8 as the mother wavelet. The impulsive noise is detected via the change in the ratio of the energy leaking out of the third level subband to the total energy of the sparse signal. Since the information in the image is redundant, the number of the noisy element is estimated within +g of the actual value. g is defined in the unit of the percent of the size of the compressed measurement signal. The experiment shows that the proposed method effectively rejected noise with large magnitude. It provided the comparable performance to the method that rejected the exact number of noisy elements but required less computational time.