Luxin Yan

Blind image deconvolution with spatially adaptive total variation regularization

A blind deconvolution algorithm with spatially adaptive total variation regularization is introduced. The spatial information in different image regions is incorporated into regularization by using the edge indicator called difference eigenvalue to distinguish edges from flat areas. The proposed algorithm can effectively reduce the noise in flat regions as well as preserve the edge and detailed information. Moreover, it becomes more robust with the change of the regularization parameter. Comparative results on simulated and real degraded images are reported.

Spectral Deconvolution and Feature Extraction With Robust Adaptive Tikhonov Regularization

Raman spectral interpretation often suffers common problems of band overlapping and random noise. Spectral deconvolution and feature-parameter extraction are both classical problems, which are known to be difficult and have attracted major research efforts. This paper shows that the two problems are tightly coupled and can be successfully solved together. Mutual support of Raman spectral deconvolution and feature-extraction processes within a joint variational framework are theoretically motivated and validated by successful experimental results. The main idea is to recover latent spectrum and extract spectral feature parameters from slit-distorted Raman spectrum simultaneously. Moreover, a robust adaptive Tikhonov regularization function is suggested to distinguish the flat, noise, and points, which can suppress noise effectively as well as preserve details. To evaluate the performance of the proposed method, quantitative and qualitative analyses were carried out by visual inspection and quality indexes of the simulated and real Raman spectra.

A real-time embedded architecture for SIFT

SIFT has shown a great success in various computer vision applications. However, its large computational complexity has been a challenge to most embedded implementations. This paper presents a low-cost embedded system based on a new architecture that successfully integrates FPGA and DSP. It optimizes the FPGA architecture for the feature detection step of SIFT to reduce the resource utilization, and optimizes the implementation of the feature description step using a high-performance DSP. Due to this novel design, this system can detect SIFT feature and extract SIFT descriptor for detected features in real-time. Extensive experiments demonstrate its effectiveness and efficiency.

Anisotropic Spectral-Spatial Total Variation Model for Multispectral Remote Sensing Image Destriping

Multispectral remote sensing images often suffer from the common problem of stripe noise, which greatly degrades the imaging quality and limits the precision of the subsequent processing. The conventional destriping approaches usually remove stripe noise band by band, and show their limitations on different types of stripe noise. In this paper, we tentatively categorize the stripes in remote sensing images in a more comprehensive manner. We propose to treat the multispectral images as a spectral-spatial volume and pose an anisotropic spectral-spatial total variation regularization to enhance the smoothness of solution along both the spectral and spatial dimension. As a result, a more comprehensive stripes and random noise are perfectly removed, while the edges and detail information are well preserved. In addition, the split Bregman iteration method is employed to solve the resulting minimization problem, which highly reduces the computational load. We extensively validate our method under various stripe categories and show comparison with other approaches with respect to result quality, running time, and quantitative assessments.

An Embedded System-on-Chip Architecture for Real-time Visual Detection and Matching

Detecting and matching image features is a fundamental task in video analytics and computer vision systems. It establishes the correspondences between two images taken at different time instants or from different viewpoints. However, its large computational complexity has been a challenge to most embedded systems. This paper proposes a new FPGA-based embedded system architecture for feature detection and matching. It consists of scale-invariant feature transform (SIFT) feature detection, as well as binary robust independent elementary features (BRIEF) feature description and matching. It is able to establish accurate correspondences between consecutive frames for 720-p (1280x720) video. It optimizes the FPGA architecture for the SIFT feature detection to reduce the utilization of FPGA resources. Moreover, it implements the BRIEF feature description and matching on FPGA. Due to these contributions, the proposed system achieves feature detection and matching at 60 frame/s for 720-p video. Its processing speed can meet and even exceed the demand of most real-life real-time video analytics applications. Extensive experiments have demonstrated its efficiency and effectiveness.

Robust destriping method with unidirectional total variation and framelet regularization

Multidetector imaging systems often suffer from the problem of stripe noise and random noise, which greatly degrade the imaging quality. In this paper, we propose a variational destriping method that combines unidirectional total variation and framelet regularization. Total-variation-based regularizations are considered effective in removing different kinds of stripe noise, and framelet regularization can efficiently preserve the detail information. In essence, these two regularizations are complementary to each other. Moreover, the proposed method can also efficiently suppress random noise. The split Bregman iteration method is employed to solve the resulting minimization problem. Comparative results demonstrate that the proposed method significantly outperforms state-of-the-art destriping methods on both qualitative and quantitative assessments.

Semi-Blind Spectral Deconvolution with Adaptive Tikhonov Regularization

Deconvolution has become one of the most used methods for improving spectral resolution. Deconvolution is an ill-posed problem, especially when the point spread function (PSF) is unknown. Non-blind deconvolution methods use a predefined PSF, but in practice the PSF is not known exactly. Blind deconvolution methods estimate the PSF and spectrum simultaneously from the observed spectra, which become even more difficult in the presence of strong noise. In this paper, we present a semi-blind deconvolution method to improve the spectral resolution that does not assume a known PSF but models it as a parametric function in combination with the a priori knowledge about the characteristics of the instrumental response. First, we construct the energy functional, including Tikhonov regularization terms for both the spectrum and the parametric PSF. Moreover, an adaptive weighting term is devised in terms of the magnitude of the first derivative of spectral data to adjust the Tikhonov regularization for the spectrum. Then we minimize the energy functional to obtain the spectrum and the parameters of the PSF. We also discuss how to select the regularization parameters. Comparative results with other deconvolution methods on simulated degraded spectra, as well as on experimental infrared spectra, are presented.

Richardson–Lucy blind deconvolution of spectroscopic data with wavelet regularization

In this work, we introduce a blind deconvolution approach with wavelet regularization for the Raman spectrum and total variation regularization for instrument function. The proposed algorithm can effectively suppress the Poisson noise as well as preserve the spectral structure information. Moreover, the split Bregman method is adopted to solve the proposed model. The comparative results on the simulated and measured Raman spectra show that the wavelet-based method outperforms the conventional methods. The deconvolution Raman spectrum is more convenient for extracting the spectral feature and interpreting the unknown chemical mixtures.

Simultaneous Destriping and Denoising for Remote Sensing Images With Unidirectional Total Variation and Sparse Representation

Remote sensing images destriping and denoising are both classical problems, which have attracted major research efforts separately. This letter shows that the two problems can be successfully solved together within a unified variational framework. To do this, we proposed a joint destriping and denoising method by integrating the unidirectional total variation and sparse representation regularizations. Experimental results on simulated and real data in terms of qualitative and quantitative assessments show significant improvements over conventional methods.

Atmospheric-Turbulence-Degraded Astronomical Image Restoration by Minimizing Second-Order Central Moment

Atmospheric turbulence affects imaging systems by virtue of wave propagation through a medium with a nonuniform index of refraction. It can lead to blurring in images acquired from a long distance away. In this letter, it is observed that blurring increases the second-order central moment (SOCM) of images, and we introduce a new parametric blur identification method by minimizing SOCM. The method applies to finite-support images, in which the scene consists of a finite-extent object against a uniformly black, gray, or white background. The SOCM method has been validated by direct comparisons with other methods on simulated and real degraded images.