noise removal operation is commonly applied as pre-processing step before subsequent image processing tasks due to the occurrence of noise during acquisition or transmission process. a common problem in imaging systems by using cmos or ccd sensors is appearance of  the salt and pepper noise. this paper presents cellular automata (ca) framework for noise removal of distorted image by the salt an...

This article gives an overview of impulse noise and its models, and points out some important and interesting facts about the study of impulse noise which are sometimes overlooked or not well understood. We discuss the different impulse noise models in the literature, focusing on their similarities and differences when applied in communications systems. The impulse noise models discussed are me...

This paper presents an augmented Lagrangian (AL) based method for designing of overcomplete dictionaries for sparse representation with general lq-data fidelity term (q 6 2). In the proposed method, the dictionary is updated via a simple gradient descent method after each inner minimization step of the AL scheme. Besides, a modified Iterated Shrinkage/Thresholding Algorithm is employed to accel...

In this paper, we introduce a novel two-stage denoising method for the removal of random-valued impulse noise (RVIN) in images. The first stage of our algorithm applies an impulse-noise detection routine that is a refinement of the HEIND algorithm and is very accurate in identifying the location of the noisy pixels. The second stage is an image inpainting routine that is designed to restore the...

Recently, so called annihilating filer-based low rank Hankel matrix (ALOHA) approach was proposed as a powerful image inpainting method. Based on the observation that smoothness or textures within an image patch corresponds to sparse spectral components in the frequency domain, ALOHA exploits the existence of annihilating filters and the associated rank-deficient Hankel matrices in the image do...

In this paper, we propose a new method for impulse noise removal from images. It uses the sparsity of images in the Discrete Cosine Transform (DCT) domain. The zeros in this domain give us the exact mathematical equation to reconstruct the pixels that are corrupted by random-value impulse noises. The proposed method can also detect and correct the corrupted pixels. Moreover, in a simpler case t...

The main objective of this project is to design an efficient architecture for removal of random-valued impulse noise from captured image. A good image de-noising model property is that, it will remove noise while preserving edges. The decision-tree-based denoising method (DTBDM) is the proposed methodology for this project. It consists of decision tree based impulse detector, to detect the nois...

A novel adaptive network fuzzy inference system (ANFIS) based filter is presented for the enhancement of images corrupted by random valued impulse noise (RVIN). This technique is performed in two steps. In the first step, impulse noise using an Asymmetric Trimmed Median Filter (ATMF). In the second step, image restoration is obtained by an appropriately combining ATMF with ANFIS at the removal ...

The classical method for impulsive noise removal in image signals consists of two steps, i.e., a step for impulse detection and a step for estimation of the pixels corrupted by impulsive noise. In this paper, we show that the step of impulse detection can be described very suitably by piecewise-linear (PWL) functions which explains intuitively the process of detection as a partition of the sign...