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Hyperspectral images(HSIs) provide hundreds of narrow spectral bands for the land-covers, thus can provide more powerful discriminative information for the land-cover classification. However, HSIs suffer from the curse of high dimensionality, therefore dimension reduction and feature extraction are essential for the application of HSIs. In this paper, we propose an unsupervised feature extraction method for HSIs using combined low rank representation and locally linear embedding (LRR LLE).

In this paper, we propose a blind motion deblurring method based on sparse representation and structural self-similarity from a single image.

The capability to automatically evaluate the quality of long wave infrared (LWIR) and visible light images has the potential to play an important role in determining and controlling the quality of a resulting fused LWIR-visible image. Extensive work has been conducted on studying the statistics of natural LWIR and visible light images. Nonetheless, there has been little work done on analyzing the statistics of fused images and associated distortions.

The capability to automatically evaluate the quality of long wave infrared (LWIR) and visible light images has the potential to play an important role in determining and controlling the quality of a resulting fused LWIR-visible image. Extensive work has been conducted on studying the statistics of natural LWIR and visible light images. Nonetheless, there has been little work done on analyzing the statistics of fused images and associated distortions.

Single materials have colors which form straight lines in RGB space. However, in severe shadow cases, those lines do not intersect the origin, which is inconsistent with the description of most literature. This paper is concerned with the detection and correction of the offset between the intersection and origin. First, we analyze the reason for forming that offset via an optical imaging model. Second, we present a simple and effective way to detect and remove the offset.

In this paper, we propose a new texture descriptor, scale selective extended local binary pattern (SSELBP), to characterize texture images with scale variations. We first utilize multiscale extended local binary patterns (ELBP) with rotation invariant and uniform mappings to capture robust local micro and macro-features. Then, we build a scale space using Gaussian filters and calculate the histogram of multi-scale ELBPs for the image at each scale.

Face recognition systems are designed to handle well-aligned images captured under controlled situations. However real-world images present varying orientations, expressions, and illumination conditions. Traditional face recognition algorithms perform poorly on such images. In this paper we present a method for face recognition adapted to real-world conditions that can be trained using very few training examples and is computationally efficient. Our method consists of performing a novel alignment process followed by classification using sparse representation techniques.

This paper proposes a novel inverse TMO, which enables to generate
HDR images from LDR ones,
not only without using any specific parameters but also at low
computing costs.
Furthermore, the inverse TMO has a new characteristic when an
LDR image is mapped from an HDR one by Reinhard's global operator.
In the case, the HDR image reconstructed by the proposed method
without parameters can be remapped into the same image as
that remapped from an HDR one reconstructed with parameters.