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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.

Compressive information acquisition is a natural approach for low-power hardware front ends, since most natural signals are sparse in some basis. Key design questions include the impact of hardware impairments (e.g., nonlinearities) and constraints (e.g., spatially localized computations) on the fidelity of information acquisition. Our goal in this paper is to obtain specific insights into such issues through modeling of a Large Area Electronics (LAE)-based image acquisition system.

Ultrasonography is a valuable diagnosis method for thyroid nodules. Automatically discriminating benign and malignant nodules in the ultrasound images can provide aided diagnosis suggestions, or increase the diagnosis accuracy when lack of experts. The core problem in this issue is how to capture appropriate features for this specific task. Here, we propose a feature extraction method for ultrasound images based on the convolution neural networks (CNNs), try to introduce more meaningful semantic features to the classification.

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.

Recently, there has been an increasing interest in end-to-end speech recognition that directly transcribes speech to text without any predefined alignments. One approach is the attention-based encoder decoder framework that learns a mapping between variable-length input and output sequences in one step using a purely data-driven method.