Image de-noising is one of the fundamental problems in the field of image processing needed for improving the image quality before performing different high-level vision tasks. Numerous wavelet based denoising methods were utilized for performing image de-noising process. In such works, there is a lack of analysis in selecting the appropriate threshold value. Moreover, such analysis leads to th...

In this study, we address the problem of noisy image super-resolution. Noisy low resolution (LR) image is always obtained in applications, while most of the existing algorithms assume that the LR image is noise-free. As to this situation, we present an algorithm for noisy image super-resolution which can achieve simultaneously image super-resolution and denoising. And in the training stage of o...

A dual tree complex wavelet transform (DT -CWT)based directional interpolation scheme for denoising of noisy images is proposed . The problems of denoising and interpolation are modeled as to estimate the noiseless and missing samples under the same framework of optimal estimation. Initially, DT -CWT is used to decompose an input low-resolution noisy wage irito low and high frequency subbands. ...

We propose a new grayscale image denoiser, dubbed as Neural Affine Image Denoiser (Neural AIDE), which utilizes neural network in a novel way. Unlike other neural network based image denoising methods, which typically apply simple supervised learning to learn a mapping from a noisy patch to a clean patch, we formulate to train a neural network to learn an affine mapping that gets applied to a n...

Intensified charge-coupled device (ICCD) images are captured by ICCD sensors in extremely low-light conditions. They often contains spatially clustered noises and general filtering methods do not work well. We find that the scale of the clustered noise in ICCD sensing images is often much smaller than that of the true structural information. Then the clustered noise can be identified by properl...

The problem of noisy image completion refers to recovering an image from a random subset of its noisy intensities. In this paper, we propose a non-local patch-based algorithm to settle the noisy image completion problem following the methodology “grouping and collaboratively filtering”. The target of “grouping” is to form patch matrices by matching and stacking similar image patches. And the “c...

conclusions in the case of existence gaussian noise, the results confirm that denoising is not effective on the measurement of t2* value. in the case of image distortion by rician noise, a predictor model is proposed to estimate the original t2* value. the predictor model is used to estimate the t2* value of new patients. the predicted t2* values were in good agreement with the corresponding or...

We developed an integration-based line detection algorithm. Existing line detection methods such as the Hough Transformation (HT) and its variants are insensitive to image noise. The reason is that HT finds lines by calculating the gradient of the image and assumes that the region where the gradient is the steepest is where lines exist. This is problematic because if an image has an extremely n...

A new approach used for image edge deduction, segmentation and normalization illumination under varying lighting conditions are presented. Edge detection refers to the process of identifying and locating sharp by applying smooth and noisy clinical technic in an image. It has favorable applications in the fields such as machine vision, pattern recognition, object recognition, motion analysis, pa...