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Subpixel target detection in hyperspectral imagery is challenging since subpixel targets are smaller in size than the resolution of a single pixel and accurate pixel-level labels on subpixel targets are often unavailable. In particular, this article addresses the problem of learning a prime prototype target signature from imprecisely labeled highly mixed hyperspectral data. Two algorithms, multiple-instance subpixel adaptive cosine estimator (MI-SPACE) and multiple-instance subpixel spectral matched filter (MI-SPSMF), based on multiple-instance learning framework are presented. The proposed methods aim to learn a discriminative prime target signature by maximizing the posterior detection statistics of subpixel hyperspectral targets for the correspondingly proposed subpixel adaptive cosine estimator (SPACE) and subpixel spectral matched filter (SPSMF) detectors, which are also developed in this article. Experimental results demonstrate the effectiveness of the proposed methods on both simulated and real-field hyperspectral subpixel target detection tasks. [ABSTRACT FROM AUTHOR]

The concept of just noticeable difference (JND), which accounts for the visibility threshold (visual redundancy) of the human visual system, is useful in perception-oriented signal processing systems. In this work, we present a comprehensive review of JND estimation technology. First, the visual mechanism and its corresponding computational modules are illustrated. These include luminance adaptation, contrast masking, pattern masking, and the contrast sensitivity function. Next, the existing pixel domain and subband domain JND models are presented and analyzed. Finally, the challenges associated with JND estimation are discussed. [ABSTRACT FROM AUTHOR]

Though blind image quality assessment (BIQA) is highly desired in perceptual-oriented image processing systems, it is extremely difficult to design a reliable BIQA method. With the help of the prior knowledge, the human visual system (HVS) hierarchically perceives the quality degradation during the visual recognition. Inspired by this, we suggest different levels of distortion generate individual degradations on hierarchical features, and propose to consider the degradations on both low and high level features for quality prediction. By mimicking the orientation selectivity (OS) mechanism in the primary visual cortex, an OS based local structure is designed for low-level visual information representation. At the meantime, the deep residual network, which possesses multiple levels for feature integration, is employed to extract the deep semantics for high-level visual content representation. By fusing the local structure and the deep semantics, a hierarchical feature set is acquired. Next, the correlations between the degradations of image qualities and their corresponding hierarchical feature sets are analyzed, and a novel hierarchical feature degradation (HFD) based BIQA (HFD-BIQA) method is built. Experimental results on the legacy and wild image quality assessment databases demonstrate the prediction accuracy of the proposed HFD-BIQA method, and verify that the HFD-BIQA performs highly consistent with the subjective perception. [ABSTRACT FROM AUTHOR]

Highlights • A novel blind image quality assessment is proposed. • The proposed method evaluates distorted image from a completely new perspective of human subjective perception. • The experiment results demonstrate the superiority of the proposed method. Abstract No-reference (NR) image quality assessment (IQA) aims to evaluate the quality of an image without reference image, which is greatly desired in the automatic visual signal processing system. Distortions degrade the visual contents and affect the semantics acquisition during the process of human perception. Although the existing methods evaluate the quality of images based on the structure, texture, or statistical characteristics, and deliver high quality prediction accuracy, they do not take the spatial semantics into account. From the perspective of human perception, distortions decrease the structural semantics that represent the structural information, and disturb the spatial semantics that describe the contents of images. Therefore, we attempt to measure the image quality by its degradation of semantics in an image. To extract the semantics of an image, a semantic network is proposed. The network contains convolutional neural networks (CNN) and Long Short-Term Memory (LSTM) that correspond to structural semantics and spatial semantics, respectively. CNN can be regarded as a coarse imitation of human visual mechanism to obtain the structural information, and LSTM can express the contents of an image. Then, by measuring the degradations of different semantics on images, a novel NR IQA is introduced. The proposed approach is evaluated on the databases of LIVE, CSIQ, TID2013, and LIVE multiply distorted database as well as LIVE in the wild image quality challenge database, and the results show superior performance to other state-of-the-art NR IQA methods. Furthermore, we explore the generalization capability of the proposed approach, and the experimental results indicate the proposed approach has a high robustness. [ABSTRACT FROM AUTHOR]