Journal ArticleDOI
A method of total variation to remove the mixed Poisson-Gaussian noise
Dang N. H. Thanh,S. D. Dvoenko +1 more
TLDR
This paper proposed a method to remove a noise that is popular in biomedicine that can be considered as a combination of Gaussian and Poisson noises, based on the total variation of an image intensity (brightness) function.Abstract:
There are many modern devices are used to create digital images. These devices use optical effects to create images. Therefore, the image quality depends on quality of optical sensors. Because of the limits of technology, these sensors cannot reconstruct the images perfectly, and always include some defects. One from these defects is noise. The noise reduces image quality and result of image processing. The image noises can be classified into some types: Gaussian noise, Poisson noise, speckle noise and so on. Depending on particular noises, we have efficient methods to remove them. There is no existing a universal method to remove all noises effectively. In this paper, we proposed a method to remove a noise that is popular in biomedicine. This noise can be considered as a combination of Gaussian and Poisson noises. Our method is based on the total variation of an image intensity (brightness) function.read more
Citations
More filters
Journal ArticleDOI
Different applied median filter in salt and pepper noise
TL;DR: DAMF could be successfully removed SAP noise at all densities and was compared with other methods by using Peak Signal to Noise Ratio (PSNR) and Structural Similarity (SSIM) for some images such as Cameraman and Lena.
Journal ArticleDOI
A new method based on pixel density in salt and pepper noise removal
Uğur Erkan,Levent Gökrem +1 more
TL;DR: A new method to remove salt and pepper noise, which is based on pixel density filter (BPDF), which shows that BPDF produces better results than the above-mentioned methods at low and medium noise density.
Journal ArticleDOI
A Review on CT and X-Ray Images Denoising Methods
TL;DR: The goal of this paper is to provide an apt choice of denoising method that suits to CT and X-ray images and to provide a review of the following important Poisson removal methods.
Journal ArticleDOI
Adaptive total variation L1 regularization for salt and pepper image denoising
TL;DR: The adaptive TV denoising method is developed based on the general regularized image restoration model with L1 fidelity for handling salt and pepper noise model and results indicate the authors obtain artifact free edge preserving restorations.
Journal ArticleDOI
Adaptive frequency median filter for the salt and pepper denoising problem
TL;DR: An adaptive frequency median filter (AFMF) is proposed to remove the salt and pepper noise and denoises more effectively than other state-of-the-art denoising methods.
References
More filters
Journal ArticleDOI
Image quality assessment: from error visibility to structural similarity
TL;DR: In this article, a structural similarity index is proposed for image quality assessment based on the degradation of structural information, which can be applied to both subjective ratings and objective methods on a database of images compressed with JPEG and JPEG2000.
Journal ArticleDOI
Nonlinear total variation based noise removal algorithms
TL;DR: In this article, a constrained optimization type of numerical algorithm for removing noise from images is presented, where the total variation of the image is minimized subject to constraints involving the statistics of the noise.
Journal ArticleDOI
No-Reference Image Quality Assessment in the Spatial Domain
TL;DR: Despite its simplicity, it is able to show that BRISQUE is statistically better than the full-reference peak signal-to-noise ratio and the structural similarity index, and is highly competitive with respect to all present-day distortion-generic NR IQA algorithms.
Book
Modern image quality assessment
Zhou Wang,A.C. Bovik +1 more
TL;DR: This book is about objective image quality assessment to provide computational models that can automatically predict perceptual image quality and to provide new directions for future research by introducing recent models and paradigms that significantly differ from those used in the past.