A COMPARATIVE STUDY OF IMAGE FILTERING ON VARIOUS NOISY PIXELS

This paper deals with the comparative study of research work done in the field of Image Filtering. Different noises can affect the image in different ways. Although various solutions are available for denoising them, a detail study of the research is required in order to design a filter which will fulfill the desire aspects along with handling most of the image filtering issues. An output image should be judged on the basis of Image Quality Metrics for ex-: Peak-Signal-to-Noise ratio (PSNR), Mean Squared Error (MSE) and Mean Absolute Error (MAE) and Execution Time

A New Fuzzy Additive Noise Reduction Method

Die Kurzsichtigkeit der Kölner Stadtpolitiker, die jetzt einen Planungsstopp für das Kölner Stadtarchiv wollen und die Integration der gemeinsamen Museumsbibliothek in Frage stellen, ist schändlich. Es geht nicht um ein Luxusarchiv, sondern darum, dass das durch die größte Archivkatastrophe in Deutschland nach 1945 gebeutelte Stadtarchiv zuverlässige bauliche Grundlagen bekommt. Unsinnig ist es, an eine Zerschlagung der gemeinsamen Museumsbibliothek zu denken, die als weiterer Nutzer des gepl..

A New Fuzzy Additive Noise Reduction Method

In this paper we present a new alternative noise reduction method for color images that were corrupted with additive Gaussian noise. We illustrate that color images have to be processed in a different way than most of the state-of-the-art methods. The proposed method consists of two sub-filters. The main concern of the first subfilter is to distinguish between local variations due to noise and local variations due to image structures such as edges. This is realized by using the color component distances instead of component differences as done by most current filters. The second subfilter is used as a complementary filter which especially preserves differences between the color components. This is realized by calculating the local differences in the red, green and blue environment separately. These differences are then combined to calculate the local estimation of the central pixel. Experimental results show the feasibility of the proposed approach

A model based on local graphs for colour images and its application for Gaussian noise smoothing

[EN] In this paper, a new model for processing colour images is presented. A graph is built for each image pixel taking into account some constraints on links. Each pixel is characterized depending on the features of its related graph, which allows to process it appropriately. As an example, we provide a characterization of each pixel based on the link cardinality of its connected component. This feature enables us to properly distinguish flat image regions respect to edge and detail regions. According to this, we have designed a hybrid filter for colour image smoothing. It combines a filter able to properly process flat image regions with another one that is more appropriate for details and texture. Experimental results show that our model performs appropriately. We also see that our proposed filter is competitive with respect to state-of-the-art methods. It is close closer to the corresponding optimal switching filter respect to other analogous hybrid method.Samuel Morillas acknowledges the support of grant MTM2015-64373-P (MINECO/FEDER, UE). Cristina Jordan acknowledges the support of grant TEC2016-79884-C2-2-R.Pérez-Benito, C.; Morillas, S.; Jordan-Lluch, C.; Conejero, JA. (2018). A model based on local graphs for colour images and its application for Gaussian noise smoothing. Journal of Computational and Applied Mathematics. 330:955-964. https://doi.org/10.1016/j.cam.2017.05.013S95596433

Deep CNN Based Skin Lesion Image Denoising and Segmentation using Active Contour Method

Automatic skin lesion segmentation on skin images is an essential component in diagnosing skin cancer. Image de-noising in skin cancer lesion is a description of processing image which refers to image restoration techniques to develop an image in predefined touch. Then de-noising is the crucial step of image processing to restore the right quality image after that which can use in many processes like segmentation, detection. This work proposes a new technique for skin lesion tumor denoising and segmentation. Initially, using Deep Convolution Neural Network (CNN) to eliminate noise and undesired structures for the images. Then, a new mechanism is proposed to segment the skin lesion into skin images based on active_contour straight with morphological processes. Different noise removal and segmentation techniques on skin lesion images are applying and comparing. The proposed algorithm shows improvement in the results of both noise reduction and segmentatio

Algoritmos paralelos para la corrección de ruido mixto gaussiano-impulsivo en imágenes digitales

Durante el proceso de adquisición o transmisión, las imágenes digitales pueden corromperse mediante ruido. Una tarea fundamental en el procesamiento digital de imágenes es la reducción de éste ruido preservando algunas características como los bordes, texturas y detalles. Dos tipos de ruido comunes son el ruido gaussiano y el ruido impulsivo, los cuales son introducidos durante los procesos de adquisición y transmisión, respectivamente. El tratamiento de imágenes de gran resolución y el filtrado de imágenes en tiempo real, el cual es necesario en gran cantidad de aplicaciones, nos conduce a requerimientos computacionales más altos. En esta investigación se diseñarán e implementarán métodos de filtrado de ruido mixto gaussiano-impulsivo haciendo uso de técnicas de computación de altas prestaciones para tratar imágenes de gran resolución y para hacer factible su ejecución en tiempo real

A study of wavelet-based noise reduction techniques in mammograms

Breast cancer is one of the most common cancers and claims over one thousand lives every day. Breast cancer turns fatal only when diagnosed in late stages, but can be cured when diagnosed in its early stages. Over the last two decades, Digital Mammography has served the diagnosis of breast cancer. It is a very powerful aid for early detection of breast cancer. However, the images produced by mammography typically contain a great amount noise from the inherent characteristics of the imaging system and the radiation involved. Shot noise or quantum noise is the most significant noise which emerges as a result of uneven distribution of incident photons on the receptor. The X-ray dose given to patients must be minimized because of the risk of exposure. This noise present in mammograms manifests itself more when the dose of X-ray radiation is less and therefore needs to be treated before enhancing the mammogram for contrast and clarity. Several approaches have been taken to reduce the amount of noise in mammograms. This thesis presents a study of the wavelet-based techniques employed for noise reduction in mammograms --Abstract, page iii

Fuzzy logic-based approach to wavelet denoising of 3D images produced by time-of-flight cameras

In this paper we present a new denoising method for the depth images of a 3D imaging sensor, based on the time-of-flight principle. We propose novel ways to use luminance-like information produced by a time-of flight camera along with depth images. Firstly, we propose a wavelet-based method for estimating the noise level in depth images, using luminance information. The underlying idea is that luminance carries information about the power of the optical signal reflected from the scene and is hence related to the signal-to-noise ratio for every pixel within the depth image. In this way, we can efficiently solve the difficult problem of estimating the non-stationary noise within the depth images. Secondly, we use luminance information to better restore object boundaries masked with noise in the depth images. Information from luminance images is introduced into the estimation formula through the use of fuzzy membership functions. In particular, we take the correlation between the measured depth and luminance into account, and the fact that edges (object boundaries) present in the depth image are likely to occur in the luminance image as well. The results on real 3D images show a significant improvement over the state-of-the-art in the field. (C) 2010 Optical Society of Americ

Study of Spatial and Transform Domain Filters for Efficient Noise Reduction

Reducing Noise in an noisy image is important pre-processing task before further processing of image like segmentation, feature extraction, texture analysis etc. Efficient Noise reduction method should retain the edges and other detailed features as much as possible. This noise gets introduced during acquisition, transmission & reception and storage & retrieval processes. As a result, there is degradation in visual quality of an image. The noises considered in this thesis Additive Gaussian White Noise (AWGN) and Multiplicative (Speckle) Noise. The main images considered are ultrasound images,where quality is reduced due to speckle noise,whih is multiplicative in nature.using the advantage of logarithmic transform it can be transformed into additive noise. Many spatial-Domain filters such as Mean filter, Median filter, Alpha-trimmed mean filter, Wiener filter, Anisotropic diffusion filter, Total variation filter, Lee filter, Bilateral filter,Circular Spatial Filter are studied and analyzed for suppression of AWGN as well as Speckle Noise. Also many Wavelet-domain filters such as Visu Shrink, Sure Shrink, Bayes Shrink,Oracle Shrink,Neigh Shrink,Smooth Shrink,Fuzzy based Wavelet Shrink are studied and analyzed under various noise conditions