Impulsive noise removal from color images with morphological filtering

This paper deals with impulse noise removal from color images. The proposed noise removal algorithm employs a novel approach with morphological filtering for color image denoising; that is, detection of corrupted pixels and removal of the detected noise by means of morphological filtering. With the help of computer simulation we show that the proposed algorithm can effectively remove impulse noise. The performance of the proposed algorithm is compared in terms of image restoration metrics and processing speed with that of common successful algorithms.Comment: The 6th international conference on analysis of images, social networks, and texts (AIST 2017), 27-29 July, 2017, Moscow, Russi

Variational Image Segmentation Model Coupled with Image Restoration Achievements

Image segmentation and image restoration are two important topics in image processing with great achievements. In this paper, we propose a new multiphase segmentation model by combining image restoration and image segmentation models. Utilizing image restoration aspects, the proposed segmentation model can effectively and robustly tackle high noisy images, blurry images, images with missing pixels, and vector-valued images. In particular, one of the most important segmentation models, the piecewise constant Mumford-Shah model, can be extended easily in this way to segment gray and vector-valued images corrupted for example by noise, blur or missing pixels after coupling a new data fidelity term which comes from image restoration topics. It can be solved efficiently using the alternating minimization algorithm, and we prove the convergence of this algorithm with three variables under mild condition. Experiments on many synthetic and real-world images demonstrate that our method gives better segmentation results in comparison to others state-of-the-art segmentation models especially for blurry images and images with missing pixels values.Comment: 23 page

Exploiting Image Local And Nonlocal Consistency For Mixed Gaussian-Impulse Noise Removal

Most existing image denoising algorithms can only deal with a single type of noise, which violates the fact that the noisy observed images in practice are often suffered from more than one type of noise during the process of acquisition and transmission. In this paper, we propose a new variational algorithm for mixed Gaussian-impulse noise removal by exploiting image local consistency and nonlocal consistency simultaneously. Specifically, the local consistency is measured by a hyper-Laplace prior, enforcing the local smoothness of images, while the nonlocal consistency is measured by three-dimensional sparsity of similar blocks, enforcing the nonlocal self-similarity of natural images. Moreover, a Split-Bregman based technique is developed to solve the above optimization problem efficiently. Extensive experiments for mixed Gaussian plus impulse noise show that significant performance improvements over the current state-of-the-art schemes have been achieved, which substantiates the effectiveness of the proposed algorithm.Comment: 6 pages, 4 figures, 3 tables, to be published at IEEE Int. Conf. on Multimedia & Expo (ICME) 201

Removal of Random Valued Impulsive Noise

In digital Image Processing, removal of noise is a highly demanded area of research.Impulsive noise is common in images which arise at the time of image acquisition and or transmission of images. Impulsive noise can be classified into two categories,namely Salt & Pepper Noise (SPN) and Random Valued Impulsive Noise (RVIN). Removal SPN is easier as compared to RVIN due to its characteristics. The present work concentrates on removal of RVIN from images.Most of the nonlinear filters used in removal of impulsive noise work in two phases,i.e. detection followed by filtering only the corrupted pixels keeping uncorrupted ones intact. Performance of such filters is dependent on the performance of detection schemes. In this work, thrust has been put to devise an accurate detection scheme and a novel weighted median filtering mechanism. The proposed detection scheme utilizes double difference among the pixels in a test window. The difference is computed along four directions namely, horizontal, vertical,and two diagonals to capture the edge direction if any exists. This helps to identify, whether the test pixels under consideration is an edge pixel or a noisy one. Subsequently, the corrupted pixels are passed through in weighted median filter, where more weights are assigned to those pixels which lie in a minimum variance direction among all the four. Extensive simulation has been carried out at various noise conditions and with different standard images. Comparative analysis has been made with existing standard schemes with suitable parameters such as Peak Signal to Noise Ratio (PSNR), fault detection and misses. It has been observed in general that the proposed schemes outperforms its counterparts at low and medium noise conditions and performs at par at high noise conditions with low computational overhead. The low computational requirements have been substantiated with number of operations required for single window operation and overall time required for detection and filtering operation. In addition, every detector utilizes a threshold value which is compared with a predefined computed value to decide whether the pixel under consideration is corrupted. Fixed threshold may perform well for one image at a particular noise condition. However, generalization is not possible for a fixed threshold. Hence, requirement for an adaptive threshold is realized. In the later part of this thesis, we propose an impulsive detection scheme using an adaptive threshold. The adaptive threshold is determined from an Artificial Neural Network (ANN) using various statistical parameters of noisy image like (µ, σ2, µ4) as inputs. The performance of this scheme is also compared with simulation results