Low-Light Enhancement in the Frequency Domain

Decreased visibility, intensive noise, and biased color are the common problems existing in low-light images. These visual disturbances further reduce the performance of high-level vision tasks, such as object detection, and tracking. To address this issue, some image enhancement methods have been proposed to increase the image contrast. However, most of them are implemented only in the spatial domain, which can be severely influenced by noise signals while enhancing. Hence, in this work, we propose a novel residual recurrent multi-wavelet convolutional neural network R2-MWCNN learned in the frequency domain that can simultaneously increase the image contrast and reduce noise signals well. This end-to-end trainable network utilizes a multi-level discrete wavelet transform to divide input feature maps into distinct frequencies, resulting in a better denoise impact. A channel-wise loss function is proposed to correct the color distortion for more realistic results. Extensive experiments demonstrate that our proposed R2-MWCNN outperforms the state-of-the-art methods quantitively and qualitatively.Comment: 8 page

Filtrage des échos ultrasonores en CND par la fonction de Cross Validation Généralisée (GCV)

Le Contrôle Non Destructif (CND) a pour objectif de répondre à plusieurs problèmes posés dans l'industrie à savoir : i) le problème de sensibilité liées à la détection de petits défauts noyés dans le bruit, ii) le problème de caractérisation qui concerne la classification des défauts détectés, iii) enfin le problème de résolution dans la séparation de défauts très rapprochés. Dans ce contexte, la détection des petits défauts noyés dans du bruit est le but principal des plus grands laboratoires de recherche en matière de CND. Dans ce travail nous introduisons l'opération de filtrage de bruit d'échos ultrasonores réels, provenant de pièces de différentes matières, dans le domaine temps-échelle. Le filtrage par seuillage est l'une des applications majeures de la transformée en Ondelettes (T.O) dans le domaine du traitement du signal. Les méthodes sous-jacentes remplacent les coefficients au-dessous d'un certain seuil par zéro et gardent ou rétrécissent le reste. Pour le choix du seuil, plusieurs méthodes existent. Nous présenterons ici, une méthode qui suppose le modèle de bruit blanc gaussien additif, cherchant un seuil qui minimise la fonction de cross validation généralisée (GCV), cette méthode présente l'avantage de ne pas avoir besoin de connaissance a priori sur la puissance du bruit qui affecte l'écho ultrasonore

SPECKLE NOISE REDUCTION USING ADAPTIVE MULTISCALE PRODUCTS THRESHOLDING

Image denoising is an essential preprocessing technique in image acquisition systems. For instance, in ultrasound (US) images, suppression of speckle noise while preserving the edges is highly preferred. Thus, in this paper denoising the speckle noise by using wavelet-based multiscale product thresholding approach is presented. The underlying principle of this technique is to apply dyadic wavelet transform and performs the multiscale products of the wavelet transform. Then, an adaptive threshold is calculated and applied to the multiscale products instead of applying it on wavelet coefficient. Thereafter, the performance of the proposed technique is compared with other denoising techniques such as Lee filter, boxcar filter, linear minimum mean square error (LMMSE) filter and median filter. The result shows that the proposed technique gives a better performance in terms of PNSR and ENL value by an average gain of 1.22 and 1.8 times the noisy on, respectively and can better preserved image detail

Denoising Acoustic Emission Signal using Wavelet Transforms for Determining the Micro Crack Location Inside of Concrete

Acoustic emission (AE) technique is developed to locate source of damage inside of concrete. However, the AE signal is interfered by much noise, which makes the determination of first time amplitude of AE signal is hard to be carried out. In fact, the determination of this parameter is a significant part for locating the source of damage in concrete. Therefore, one of the denoising methods called wavelet based denoising is proposed. In this case, some wavelet bases function are investigated to find out the proper wavelet bases function to perform the denoising of AE Signal. From the experimental data, the best wavelet basis function for this case is Coiflet, which is shown by providing the best SNR than the other wavelet families. In addition, the determining cracks locations on concrete can be performed easier on denoised AE signal than on noisy AE signal

Improving the Performance of the Prony Method Using a Wavelet Domain Filter for MRI Denoising

The Prony methods are used for exponential fitting. We use a variant of the Prony method for abnormal brain tissue detection in sequences of T2 weighted magnetic resonance images. Here, MR images are considered to be affected only by Rician noise, and a new wavelet domain bilateral filtering process is implemented to reduce the noise in the images. This filter is a modification of Kazubek’s algorithm and we use synthetic images to show the ability of the new procedure to suppress noise and compare its performance with respect to the original filter, using quantitative and qualitative criteria. The tissue classification process is illustrated using a real sequence of T2 MR images, and the filter is applied to each image before using the variant of the Prony method

Determination of parameters associated to the wavelet filter by umbralization applied to filtered electrocardiographic interferences

The parameters of a wavelet shrinkage fltering system for ECG signal treatment are discussed and evaluated. The studied parameters are: wavelet order, number of decomposition levels to evaluate, threshold estimator type, wavelet family type (shift variant or shift invariant), and wavelet family. The study includes a revision of the state of the art and experimental verifcations. A reliable system is defned in this way, and not only based in the morphologic considerations and similarities between the ECG signal and the wavelet function.Se discuten y evalúan los parámetros implicados en un sistema de filtrado por umbralización basado en transformada wavelet (shrinkage), con el objetivo de definirlos para el tratamiento de señales electrocardiográficas. Los parámetros estudiados son: orden de las familias wavelet, número de niveles de descomposición a umbralizar, tipo de estimador de umbral, tipo de transformada wavelet (variante o invariante al desplazamiento) y finalmente elección de una familia wavelet. El estudio involucra una revisión del estado del arte y comprobaciones experimentales. Con esto se logra proponer un sistema confiable, y no solo basado en la similitud de la señal ECG con la función wavelet.&nbsp

SPECKLE NOISE REDUCTION USING ADAPTIVE MULTISCALE PRODUCTS THRESHOLDING

Image denoising is an essential preprocessing technique in image acquisition systems. For instance, in ultrasound (US) images, suppression of speckle noise while preserving the edges is highly preferred. Thus, in this paper denoising the speckle noise by using wavelet-based multiscale product thresholding approach is presented. The underlying principle of this technique is to apply dyadic wavelet transform and performs the multiscale products of the wavelet transform. Then, an adaptive threshold is calculated and applied to the multiscale products instead of applying it on wavelet coefficient. Thereafter, the performance of the proposed technique is compared with other denoising techniques such as Lee filter, boxcar filter, linear minimum mean square error (LMMSE) filter and median filter. The result shows that the proposed technique gives a better performance in terms of PNSR and ENL value by an average gain of 1.22 and 1.8 times the noisy on, respectively and can better preserved image detail