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For regression-based single-image super-resolution (SR) problem, the key is to establish a mapping relation between high-resolution (HR) and low-resolution (LR) image patches for obtaining a visually pleasing quality image. Most existing approaches typically solve it by dividing the model into several single-output regression problems, which obviously ignores the circumstance that a pixel within an...
Over the past years, research on digital image forensics has become a hot topic in multimedia security. Among various forensics technologies, image resampling detection has become a standard detection tool in image forensics. Furthermore, examining parameters of geometric transformations such as scaling factors or rotation angles is very useful for exploring an image's overall processing history....
The existing face sketch-photo synthesis methods trend to lose some vital details more or less. In this paper, we propose a novel sketch-photo synthesis approach based on support vector regression (SVR) to handle this difficulty. First, we utilize an existing method to acquire the initial estimate of the synthesized image. Then, the final synthesized image is obtained by combining the initial estimate...
In order to resolve the problem incurred by low efficient manual classification of tremendous aurora images, an automatic aurora images classification system for huge dataset application is proposed. First, static aurora images are decomposed into texture part and cartoon part with a method called Morphological Component Analysis (MCA). Then features extracted from texture part are classified by three...
In this paper we discuss a new approach for the detection of clustered microcalcifications (MCs) in mammograms. MCs are an important early sign of breast cancer in women. Their accurate detection is a key issue in computer aided detection scheme. To improve the performance of detection, we propose a Bagging and Boosting based twin support vector machine (BB-TWSVM) to detect MCs. The algorithm is composed...
This paper presents a novel approach to microcalcification clusters (MCs) detection in mammograms based on the tensor subspace learning and twin support vector machines (TWSVMs). The ground truth of MCs in mammograms is assumed to be known as a priori. First each MCs is enhanced by using a simple artifact removal filter and a well designed high-pass filter. Then the tensor subspace learning algorithms,...
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