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A patch-based noise level estimation algorithm is proposed in this paper, with patches generated from a single noisy image. One can easily estimate the noise level from image patches using principal component analysis (PCA) if the image comprises only weak textured patches. The challenge for patch-based noise level estimation is how to select weak textured(More)
Noise level is an important parameter to many image processing applications. For example, the performance of an image denoising algorithm can be much degraded due to the poor noise level estimation. Most existing denoising algorithms simply assume the noise level is known that largely prevents them from practical use. Moreover, even with the given true(More)
The additive white Gaussian noise (AWGN) is usually assumed in many image processing algorithms. However, these algorithms cannot effectively deal with the noise from actual cameras which is better modeled as signal dependent noise (SDN). In this paper, we focus on the SDN model and propose an algorithm to accurately estimate its parameters without any(More)
The additive white Gaussian noise is widely assumed in many image processing algorithms. However, in the real world, the noise from actual cameras is better modeled as signal-dependent noise (SDN). In this paper, we focus on the SDN model and propose an algorithm to automatically estimate its parameters from a single noisy image. The proposed algorithm(More)
Acute lung injury (ALI) induced by systemic inflammatory response syndrome (SIRS) is characterized by deterioration in pulmonary function and leukocyte-associated lung inflammation. Actin fragment (F-actin) reorganization is required for leukocyte activation, adhesion, and transcription of inflammatory factors. We tested the hypothesis that F-actin plays a(More)
The recognition of text in natural scene images is a practical yet challenging task due to the large variations in backgrounds, textures, fonts, and illumination conditions. In this paper, we propose a highly accurate character recognition model by utilizing the representational power of a specially designed Convolutional Neural Network (CNN). Based on the(More)
In order to enhance fuel economy of hybrid excavator system, a control strategy based on equivalent fuel consumption is introduced. Efficiency of motor and ultra-capacitor is converted to fuel consumption of engine, which is used to establish the equivalent fuel consumption function of the whole system and further to optimize the power distribution. A(More)
State-of-the-art image denoising algorithms usually assume additive white Gaussian noise (AWGN), although they have achieved outstanding performance, modeling and removing real signal dependent noise from a single image still remains a challenging problem. In this paper we propose a segmentation-based image denoising algorithm for signal dependent noise.(More)
Several metal complexes [(FeII(DPAH)2 (DPAH2 = 2,6-dicarboxyl pyridine), FeII(PA)2 (PAH = picolinic acid), FeII(bpy)2(2+), FeII(OPPh3)4(2+), (Cl8TPP)FeIIIX (X = Cl, OH, SCH2Ph) [Cl8TPP = tetrakis (2,6-dichlorophenyl)porphyrin], (TPP) FeIIICl (TPP = tetraphenylporphyrin), and CuI(tpy)2+ (typ = 2,2'-6,2"-terpyridine)] in combination with one of several(More)
Irregular scene text such as curved, rotated or perspective texts commonly appear in natural scene images due to different camera view points, special design purposes etc. In this work, we propose a text salience map guided model to recognize these arbitrary direction scene texts. We train a deep Fully Convolutional Network (FCN) to calculate the precise(More)