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This paper propose an improved image filtering algorithm which is based on median filteringing algorithm and medium filteringing algorithm according to the simpleness of median filteringing algorithm and the significant denoising effect of medium filteringing algorithm. The new algorithm combines the two algorithms and thus gets a better filtering effect. We did the simulation using MATLAB, and then...
In this paper, we propose a novel blind image restoration method based on total variation (TV) regularization. It involves alternate iteration of point spread function (PSF) estimation and deconvolution. Using this method, we can obtain clear images from blurred images without an increase in noise and ringing. Thus, blurred images captured using digital cameras can be restored effectively.
A novel method to detect and remove adherent noises in videos from a moving camera is presented in this paper. The basic idea is to detect the adherent noises by spatio-temporal image processing technology first and then remove and restore the information in noise regions using a 3D inpainting technology. Under the condition that camera motion is unknown and unconstrained, a 3D spatio-temporal image...
Fuzzy sets which capture the meaning representation of linguistic variables have been widely used in image processing in the last decades. Fuzzy sets are associated with vagueness which is type 1 uncertainty. Interval-valued fuzzy sets (IVFS) are associated with type 2 semantic uncertainty. Indeed, the length of the interval provides the "‘non-specifity" measure for IVFS. We investigate...
In order to reduce the much unknown noise and improve the resolution of images acquired by millimeter wave imaging system, and combined the advantages of fuzzy theory and neural network, a new MMW image restoration method using a modified Takagi-Sugeno (T-S) fuzzy neural network model is proposed in this paper. The modified T-S fuzzy neural network has the excellent ability of adaptive learning, nonlinear...
In this paper, we present a denoising method based on the frequency domain Wiener filter for implementation in the blind condition. We aim at preserving fine details and edges while suppressing noise, and efficient computational cost. This method consist of a two-phase process where the noise and image power spectra are first estimated from a noisy image and employed for the frequency domain Wiener...
Ordinary Lucy-Richardson (LR) restoration algorithms are used to restore high SNR degraded images including astronomical images and achieve good results. The algorithms are very sensitive to noises and use the assumption—noises observe the Possion distribution. However, there are always Gaussian noises in natural images. In this paper, we propose a regularization LR-algorithm based on the Gaussian...
A new fast monotonic blind deconvolution algorithmic method is investigated based on the constrained variational minimization framework under the periodic boundary conditions. The contributions of our methodology are that the blur operator identification and image restoration can be simultaneously optimized even under high noise level as compared to previous methods. Specifically, the monotone fast...
This paper describes a novel strategy to enhance underwater videos and images. Built on the fusion principles, our strategy derives the inputs and the weight measures only from the degraded version of the image. In order to overcome the limitations of the underwater medium we define two inputs that represent color corrected and contrast enhanced versions of the original underwater image/frame, but...
Regularization-based methods have been found widespread application in image deconvolution. Local regularization approaches have made outstanding performance for edge maintain, such as total variation regularization. However, they have weakness in textures preserving. To handle images of hybrid edges and textures, we propose an iterative regularization method that utilizes weighted patches along with...
The Richardson-Lucy algorithm is one of the most important algorithms in the image deconvolution area. However, one of its drawbacks is slow convergence. A very significant acceleration is obtained by the technique proposed by Biggs and Andrews (BA), which is implemented in the deconvlucy function of the Image Processing MATLAB toolbox. The BA method was developed heuristically with no proof of convergence...
Ultrasound image resolution enhancement is an ongoing challenge to date. Though many works have been performed using device-based approach, there exists few works dealing with post-processing methods. This paper investigates a technique based on the Alternating Direction Method of Multipliers for the resolution enhancement in ultrasound imaging, which includes the deblurring and denoising tasks. We...
In this article, we are interested in restoring images from 3D fluorescence microscopy. In fact, these images are affected by a depth-variant blur due to light refraction phenomenon. We present and compare two different restoration strategies for that problem. The first one is based on multiple deconvolutions with depth-invariant blur functions and the second one consists in using a depth-variant...
Due to abrupt increase in interacting strength of probed molecules and the AFM tip, the dramatic change in tip vibrations leads to a loss or inadequate acquisition of height information during the scanning across the sample surfaces. Consequently, stripe noises occur and immediately become the first encountered obstacle for characterizing objects in AFM images. The un-supervised DeStripe has been...
In this work, we introduce a new approach for the signal deconvolution problem, which is useful for the enhancement of neutron radiography projections. We attempt to restore original signals and get rid of noise present during acquisition or processing, due to gamma radiations or randomly distributed neutron flux. Signal deconvolution is an ill-posed inverse problem, so regularization techniques are...
In this paper, we consider the problem to recover a blurred and noisy binary images when the point spread function (PSF) is known. By explicitly using the a priori knowledge for binary image in the constrained minimization model, we present a fast algorithm for binary image restoration. The main computation at each iteration is one Fourier transform (FFT) and one inverse fast Fourier transform (IFFT)...
In this paper, a modified decision based median filtering approach is presented for the restoration of gray scale and color images that are highly corrupted by salt and pepper noise. It is an enhanced decision based algorithm where noise pixels are detected in several phases based on predefined threshold value. The noise pixels are replaced by median where median value is calculated without considering...
In this paper we propose a blind deconvolution algorithm for wide field fluorescence microscopy. The 3D PSF is modeled after a parametrized pupil function. The PSF parameters are estimated jointly with the object in a maximum a posteriori framework. We illustrate the performances of our algorithm on experimental data and show significant resolution improvement notably along the depth. Quantitative...
So far it is still difficult to remove random-valued impulse noise excellently at any noise level for any image. In this paper, we present a novel robust two-phase denoising scheme based on smooth and texture region separation. In the first phase, all pixels are classified into two groups by using cascade window filtering: the smooth region pixels and the texture region pixels. Then we identify pixels...
Noise is ever-present in communication channels and can produce devastating degradation to the images at the receiver end. In this paper, a novel approach to detecting different types of noise models in corrupted images is presented. Basically, it is a two-step process that has the ability to handle real-time applications due to its computational simplicity. The proposed algorithm handles error correction...
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