Penumbral imaging is a kind of technique which uses the facts that spatial information can be recovered from the shadow or penumbra that an unknown source casts through a simple large circular aperture. The technique is based on a linear deconvolution. As we known that the information of the penumbral image is only contained in the penumbra (the edges of the image), so according to that principle, we proposed a two-step method for decoding penumbral images in this paper. First, an edgy-emphasizing algorithm using a band filter is applied to extract the penumbras (the image edges) in noisy penumbral images; then, followed by conventional linear deconvolution of only the penumbral edges. The simulation results show that the reconstructed image is dramatically improved in comparison to that with the conventional noise-removing filters, and the proposed method is also applied to real experimental x-ray imaging.
CT (computed tomography) imaging is a technology which uses X-ray beams (radiation) and computers to form detailed,
cross-sectional images of an area of anatomy. However, the random scattered X-ray in CT imaging system will reduce
radiographic contrast greatly in CT images. In this paper, a four-step method is proposed for decoding CT images: first,
the EGSnrc Monte Carlo simulation system is used to simulate CT imaging and simulated data will be validated by real
experimental data in the same experimental conditions; second, scattered X-ray image simulated by EGSnrc will be
transformed into ICA-domain (independent component analysis-domain) to obtain the main magnitude of scattering data;
third, a noise-reduction algorithm based on ICA-domain shrinkage is applied to smooth the CT image; fourth, the
conventional linear deconvolution follows. The simulation results show that the reconstructed image is dramatically
improved in comparison to that without the noise-removing filters, and the proposed method is also applied to real
experimental X-ray imaging.
Radiological imaging such as x-ray CT is one of the most important tools for medical diagnostics. Since the radiological images are always with some quantum noise and the reduction of quantum noise or Poisson noise in medical images is an important issue. In this paper, we propose a new filtering based on independent component analysis (ICA) for reduction of noise. In the proposed filtering, the image (projection) is first transformed to ICA domain and then the components of scattered x-ray are removed by a soft thresholding (Shrinkage). The proposed method has been demonstrated by using both standard images and Monte Carlo simulations. Experimental results show that the quality of the image can be dramatically improved without any blurring in edge by the proposed filter
This paper proposes a new method to denoise images corrupted by Poisson noise. Poisson noise is signal-dependent, and consequently, separating signals from noise is a very difficult task. In most current Poisson noise reduction algorithms, noise signal are pre-processed to approximate Gaussian noise, and then denoised by a conventional Gaussian denoising algorithm. In this paper, we propose to use adaptive basis functions derived from the data using modified ICA (Independent Component Analysis), and a maximum likelihood shrinkage algorithm based on the property of Poisson noise. This modified ICA method is based on a denoising method called "Sparse Code Shrinkage (SCS)" and wavelet-domain denoising. In denoising procedure of ICA-domain, the shrinkage function is determined by the property of Poisson noise that adapts to the intensity of signal. The performance of the proposed algorithm is validated with simulated data experiments, and the results demonstrate that the algorithm greatly improves the denoising performance in images contaminated by Poisson noise.