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The generative adversarial network (GAN) is successfully applied to study the perceptual single image super-resolution (SISR). However, since the GAN is data-driven, it has a fundamental limitation on restoring real high frequency information for an unknown instance (or image) during test. On the other hand, the conventional model-based methods have a superiority to achieve instance adaptation as they operate by considering the statistics of each instance (or image) only. Motivated by this, we propose a novel model-based algorithm, which can extract the detail layer of an image efficiently. The detail layer represents the high frequency information of image and it is constituted of image edges and fine textures. It is seamlessly incorporated into the GAN and serves as a prior knowledge to assist the GAN in generating more realistic details. The proposed method, named DSRGAN, takes advantages from both the model-based conventional algorithm and the data-driven deep learning network. Experimental results demonstrate that the DSRGAN outperforms the state-of-the-art SISR methods on perceptual metrics, meanwhile achieving comparable results in terms of fidelity metrics. Following the DSRGAN, it is feasible to incorporate other conventional image processing algorithms into a deep learning network to form a model-based deep SISR. [ABSTRACT FROM AUTHOR]

Vehicle detection in aerial images is a crucial image processing step for many applications such as screening of large areas as used for surveillance, reconnaissance, or rescue tasks. In recent years, several deep learning-based frameworks have been proposed for object detection. However, these detectors were developed for data sets that considerably differ from aerial images. In this paper, we systematically investigate the potential of fast R-CNN and faster R-CNN for aerial images, which achieve top performing results on common detection benchmark data sets. Therefore, the applicability of eight state-of-the-art object proposal methods used to generate a set of candidate regions and of both detectors is examined. Relevant adaptations to account for the characteristics of the aerial images are provided. To overcome the shortcomings of the original approach in the case of handling small instances, we further propose our own networks that clearly outperform state-of-the-art methods for vehicle detection in aerial images. Furthermore, we analyze the impact of the different adaptations with respect to various ground sampling distances to provide a guideline for detecting small objects in aerial images. All experiments are performed on two publicly available data sets to account for differing characteristics such as varying object sizes, number of objects per image, and varying backgrounds. [ABSTRACT FROM AUTHOR]

Achieving good text detection and recognition results for multi-script-oriented images is a challenging task. First, we explore bit plane slicing in order to utilize the advantage of the most significant bit information to identify text components. A new iterative nearest neighbor symmetry is then proposed based on shapes of convex and concave deficiencies of text components in bit planes to identify candidate planes. Further, we introduce a new concept called mutual nearest neighbor pair components based on gradient direction to identify representative pairs of texts in each candidate bit plane. The representative pairs are used to restore words with the help of edge image of the input one, which results in text detection results (words). Second, we propose a new idea by fixing window for character components of arbitrary oriented words based on angular relationship between sub-bands and a fused band. For each window, we extract features in contourlet wavelet domain to detect characters with the help of an SVM classifier. Further, we propose to explore HMM for recognizing characters and words of any orientation using the same feature vector. The proposed method is evaluated on standard databases such as ICDAR, YVT video, ICDAR, SVT, MSRA scene data, ICDAR born digital data, and multi-lingual data to show its superiority to the state of the art methods. [ABSTRACT FROM AUTHOR]

Contrast enhancement is a crucial image processing step for image quality control. However, images enhanced by conventional contrast enhancement methods can have negative effects on the performance of image quality control. The most commonly observed effects are over- and under-enhancement effects on images, which cause significant loss of fine textures in images. This paper developed a new contrast enhancement algorithm based on an entropy-preserving mapping prior that improves on conventional contrast enhancement methods. By creating a closed-form solution for enhancing the image contrast under this novel prior and learning the coefficients of the solution using an unsupervised learning strategy, an image’s contrast and texture can be effectively recovered. The experimental results verify that our proposed method clearly outperforms the existing state-of-the-art methods in terms of both quantitative estimation and qualitative human visual inspection. [ABSTRACT FROM AUTHOR]