Colour image smoothing through a soft-switching mechanism using a graph model

[EN] In this study, the authors propose a soft-switching ¿lter to improve the performance of recent colour image smoothing ¿lters when processing homogeneous image regions. The authors use a recent ¿lter mixed with the classical arithmetic mean ¿lter (AMF). The recent method is used to process image pixels close to edges, texture and details and the AMF is only used to process homogeneous regions. To this end, the authors propose a method based on the graph theory to distinguish image details and homogeneous regions and to perform a soft switching between the two ¿lters. Experimental results show that the proposed method provides improved results which supports the appropriateness of the graph theory-based method and suggests that the same structure can be used to improve the performance of other non-linear colour image smoothing methods.The authors acknowledge the support of Spanish Ministry of Science and Innovation under grant MTM2009-12872-C02-01, Spanish Ministry of Science and Technology under grant MTM2010-18539 and DGCYT under grant MTM2009-08933Jordan Lluch, C.; Morillas, S.; Sanabria Codesal, E. (2012). Colour image smoothing through a soft-switching mechanism using a graph model. IET Image Processing. 6(9):1293-1298. doi:10.1049/IET-IPR.2011.0164S129312986

Denoising of Natural Images Using the Wavelet Transform

A new denoising algorithm based on the Haar wavelet transform is proposed. The methodology is based on an algorithm initially developed for image compression using the Tetrolet transform. The Tetrolet transform is an adaptive Haar wavelet transform whose support is tetrominoes, that is, shapes made by connecting four equal sized squares. The proposed algorithm improves denoising performance measured in peak signal-to-noise ratio (PSNR) by 1-2.5 dB over the Haar wavelet transform for images corrupted by additive white Gaussian noise (AWGN) assuming universal hard thresholding. The algorithm is local and works independently on each 4x4 block of the image. It performs equally well when compared with other published Haar wavelet transform-based methods (achieves up to 2 dB better PSNR). The local nature of the algorithm and the simplicity of the Haar wavelet transform computations make the proposed algorithm well suited for efficient hardware implementation

Colour image denoising by eigenvector analysis of neighbourhood colour samples

[EN] Colour image smoothing is a challenging task because it is necessary to appropriately distinguish between noise and original structures, and to smooth noise conveniently. In addition, this processing must take into account the correlation among the image colour channels. In this paper, we introduce a novel colour image denoising method where each image pixel is processed according to an eigenvector analysis of a data matrix built from the pixel neighbourhood colour values. The aim of this eigenvector analysis is threefold: (i) to manage the local correlation among the colour image channels, (ii) to distinguish between flat and edge/textured regions and (iii) to determine the amount of needed smoothing. Comparisons with classical and recent methods show that the proposed approach is competitive and able to provide significative improvements.Latorre-Carmona, P.; Miñana, J.; Morillas, S. (2020). Colour image denoising by eigenvector analysis of neighbourhood colour samples. 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Robust perceptual organization techniques for analysis of color images

Esta tesis aborda el desarrollo de nuevas técnicas de análisis robusto de imágenes estrechamente relacionadas con el comportamiento del sistema visual humano. Uno de los pilares de la tesis es la votación tensorial, una técnica robusta que propaga y agrega información codificada en tensores mediante un proceso similar a la convolución. Su robustez y adaptabilidad han sido claves para su uso en esta tesis. Ambas propiedades han sido verificadas en tres nuevas aplicaciones de la votación tensorial: estimación de estructura, detección de bordes y segmentación de imágenes adquiridas mediante estereovisión.El mayor problema de la votación tensorial es su elevado coste computacional. En esta línea, esta tesis propone dos nuevas implementaciones eficientes de la votación tensorial derivadas de un análisis en profundidad de esta técnica.A pesar de su capacidad de adaptación, esta tesis muestra que la formulación original de la votación tensorial (a partir de aquí, votación tensorial clásica) no es adecuada para algunas aplicaciones, dado que las hipótesis en las que se basa no se ajustan a todas ellas. Esto ocurre particularmente en el filtrado de imágenes en color. Así, esta tesis muestra que, más que un método, la votación tensorial es una metodología en la que la codificación y el proceso de votación pueden ser adaptados específicamente para cada aplicación, manteniendo el espíritu de la votación tensorial.En esta línea, esta tesis propone un marco unificado en el que se realiza a la vez el filtrado de imágenes y la detección robusta de bordes. Este marco de trabajo es una extensión de la votación tensorial clásica en la que el color y la probabilidad de encontrar un borde en cada píxel se codifican mediante tensores, y en el que el proceso de votación se basa en un conjunto de criterios perceptuales relacionados con el modo en que el sistema visual humano procesa información. Los avances recientes en la percepción del color han sido esenciales en el diseño de dicho proceso de votación.Este nuevo enfoque ha sido efectivo, obteniendo excelentes resultados en ambas aplicaciones. En concreto, el nuevo método aplicado al filtrado de imágenes tiene un mejor rendimiento que los métodos del estado del arte para ruido real. Esto lo hace más adecuado para aplicaciones reales, donde los algoritmos de filtrado son imprescindibles. Además, el método aplicado a detección de bordes produce resultados más robustos que las técnicas del estado del arte y tiene un rendimiento competitivo con relación a la completitud, discriminabilidad, precisión y rechazo de falsas alarmas.Además, esta tesis demuestra que este nuevo marco de trabajo puede combinarse con otras técnicas para resolver el problema de segmentación robusta de imágenes. Los tensores obtenidos mediante el nuevo método se utilizan para clasificar píxeles como probablemente homogéneos o no homogéneos. Ambos tipos de píxeles se segmentan a continuación por medio de una variante de un algoritmo eficiente de segmentación de imágenes basada en grafos. Los experimentos muestran que el algoritmo propuesto obtiene mejores resultados en tres de las cinco métricas de evaluación aplicadas en comparación con las técnicas del estado del arte, con un coste computacional competitivo.La tesis también propone nuevas técnicas de evaluación en el ámbito del procesamiento de imágenes. En concreto, se proponen dos métricas de filtrado de imágenes con el fin de medir el grado en que un método es capaz de preservar los bordes y evitar la introducción de defectos. Asimismo, se propone una nueva metodología para la evaluación de detectores de bordes que evita posibles sesgos introducidos por el post-procesado. Esta metodología se basa en cinco métricas para estimar completitud, discriminabilidad, precisión, rechazo de falsas alarmas y robustez. Por último, se proponen dos nuevas métricas no paramétricas para estimar el grado de sobre e infrasegmentación producido por los algoritmos de segmentación de imágenes.This thesis focuses on the development of new robust image analysis techniques more closely related to the way the human visual system behaves. One of the pillars of the thesis is the so called tensor voting technique. This is a robust perceptual organization technique that propagates and aggregates information encoded by means of tensors through a convolution like process. Its robustness and adaptability have been one of the key points for using tensor voting in this thesis. These two properties are verified in the thesis by applying tensor voting to three applications where it had not been applied so far: image structure estimation, edge detection and image segmentation of images acquired through stereo vision.The most important drawback of tensor voting is that its usual implementations are highly time consuming. In this line, this thesis proposes two new efficient implementations of tensor voting, both derived from an in depth analysis of this technique.Despite its adaptability, this thesis shows that the original formulation of tensor voting (hereafter, classical tensor voting) is not adequate for some applications, since the hypotheses from which it is based are not suitable for all applications. This is particularly certain for color image denoising. Thus, this thesis shows that, more than a method, tensor voting can be thought of as a methodology in which the encoding and voting process can be tailored for every specific application, while maintaining the tensor voting spirit.By following this reasoning, this thesis proposes a unified framework for both image denoising and robust edge detection.This framework is an extension of the classical tensor voting in which both color and edginess the likelihood of finding an edge at every pixel of the image are encoded through tensors, and where the voting process takes into account a set of plausible perceptual criteria related to the way the human visual system processes visual information. Recent advances in the perception of color have been essential for designing such a voting process.This new approach has been found effective, since it yields excellent results for both applications. In particular, the new method applied to image denoising has a better performance than other state of the art methods for real noise. This makes it more adequate for real applications, in which an image denoiser is indeed required. In addition, the method applied to edge detection yields more robust results than the state of the art techniques and has a competitive performance in recall, discriminability, precision, and false alarm rejection.Moreover, this thesis shows how the results of this new framework can be combined with other techniques to tackle the problem of robust color image segmentation. The tensors obtained by applying the new framework are utilized to classify pixels into likely homogeneous and likely inhomogeneous. Those pixels are then sequentially segmented through a variation of an efficient graph based image segmentation algorithm. Experiments show that the proposed segmentation algorithm yields better scores in three of the five applied evaluation metrics when compared to the state of the art techniques with a competitive computational cost.This thesis also proposes new evaluation techniques in the scope of image processing. First, two new metrics are proposed in the field of image denoising: one to measure how an algorithm is able to preserve edges, and the second to measure how a method is able not to introduce undesirable artifacts. Second, a new methodology for assessing edge detectors that avoids possible bias introduced by post processing is proposed. It consists of five new metrics for assessing recall, discriminability, precision, false alarm rejection and robustness. Finally, two new non parametric metrics are proposed for estimating the degree of over and undersegmentation yielded by image segmentation algorithms

TO BE SUBMITTED TO IEEE TRANSACTIONS ON IMAGE PROCESSING 1 Image Denoising using Derotated Complex Wavelet Coefficients

A method for removing additive Gaussian noise from digital images is described. It is based on statistical modelling of the coefficients of a redundant, oriented, complex multi-scale transform. Two types of modelling are used to model the wavelet coefficients. Both are based on Gaussian Scale Mixture (GSM) modelling of neighbourhoods of coefficients at adjacent locations and scales. Modelling of edge and ridge discontinuities is performed using wavelet coefficients derotated by twice the phase of the coefficient at the same location and the next coarser scale. Other areas are modelled using standard wavelet coefficients. An adaptive Bayesian model selection framework is used to determine the modelling applied to each neighbourhood. The proposed algorithm succeeds in providing improved denoising performance at structural image features, reducing ringing artifacts and enhancing sharpness, while avoiding degradation in other areas. The method outperforms previously published methods visually and in standard tests