基于稀疏贝叶斯学习的雷达目标成像技术

利用傅立叶变换进行雷达目标成像,分辨率受瑞利准则的限制;超分辨成像技术能最著改善雷达图像的分辨率,但算法的复杂性急剧增加并且正则化参数不易选取.以稀疏贝叶斯学习为基础,针对雷达成像系统的结构特点,提出了一种基于快速傅立叶变换(FFT)和分块托普里兹(Toeplitz)系统的快速超分辨成像算法.算法无需存储系数矩阵,极大地降低了存储量和运算量.进一步,通过寻找拟合误差曲线和稀疏性度量函数曲线的交点实现了正则化参数的方便选择.仿真结果表明,算法对雷达目标图像具有良好的分辨率增强能力.     

Total least square kernel regression

In this paper, we study the problem of robust image fusion in the context of multi-frame super-resolution. Given multiple aligned noisy low-resolution images, image fusion produces a new image on a high-resolution grid. Recently, kernel regression is presented as a powerful image fusion technique. However, in the presence of registration errors, the performance of kernel regression is quite poor. Therefore, we present a new kernel regression method that takes these registration errors into account. Instead of the ordinary least square metric, we employ the total least square metric, which allows for spatial perturbations of the image samples. We show in our experiments that our method is more robust to noise and/or registration errors compared to the traditional kernel regression algorithm.     

采用超分辨率重建提升压缩图像质量的方法

针对JPEG的中低码率压缩图像即高压缩率图像存在较严重的块效应以及量化噪声,提出了一种对JPEG标准压缩图像进行优化的重建-采样方法.该方法对JPEG压缩图像采用三维块匹配算法(BM3D)进行去噪,去除图像中存在的块效应和量化噪声,进而提高超分辨率重建的映射准确性,再使用外部库对去噪后图像进行基于稀疏表示的超分辨率重建,补充一定的高频信息,最后对重建后的高分辨率图进行双三次下采样,得到与原始图像大小一致的图像作为最终优化图像.实验结果表明,该方法在中低码率情况下能够有效地提高JPEG压缩图像的质量,对高码率压缩图像也有一定效果.     

下采样对复眼图像超分辨率重构效果的影响

针对复眼超分辨率重构系统中,当以不同成像分辨率(对应不同的下采样因子)的器件拍摄同一场景时,重构效果相对于各自低分辨率图像改善程度不同的问题,研究了下采样程度与复眼超分辨率重构效果之间的关系。通过仿真实验获取不同下采样因子下低分辨率图像的重构结果,从信息熵、信噪比和峰值信噪比对重构前后图像质量进行评价,并采用Romchi Ruling分辨率靶板对仿真结果进行实验验证。实验结果表明:以3至4为下采样因子对512×512的lena图像采样时,信噪比提高7.29db,重构效果改善明显;以相对下采样因子2.2对50mm×50mm的Romchi Ruling分辨率靶板采样时,分辨率提高3个等级。其研究结果可用于指导复眼成像系统研制过程中对成像器件的选型。     

Hallucinating faces: LPH super-resolution and neighbor reconstruction for residue compensation

A two-phase face hallucination approach is proposed in this paper to infer high-resolution face image from the low-resolution observation based on a set of training image pairs. The proposed locality preserving hallucination (LPH) algorithm combines locality preserving projection (LPP) and radial basis function (RBF) regression together to hallucinate the global high-resolution face. Furthermore, in order to compensate the inferred global face with detailed inartificial facial features, the neighbor reconstruction based face residue hallucination is used. Compared with existing approaches, the proposed LPH algorithm can generate global face more similar to the ground truth face efficiently, moreover, the patch structure and search strategy carefully designed for the neighbor reconstruction algorithm greatly reduce the computational complexity without diminishing the quality of high-resolution face detail. The details of synthetic high-resolution face are further improved by a global linear smoother. Experiments indicate that our approach can synthesize distinct high-resolution faces with various facial appearances such as facial expressions, eyeglasses efficiently.     

Solving a two-colour problem by applying probabilistic approach to a full-colour multi-frame image super-resolution

Reconstruction based algorithms play an important role in the multi-frame super-resolution problem. A group of images of the same scene are fused together to produce an image with higher spatial resolution, or with more visible details in the high spatial frequency features. Demosaicing algorithms interpolate missing pixels in a raw image taken from one Charged Coupled Device (CCD) array, upsampling the number of the pixels present in the image. Since super-resolution (SR) and demosaicing are the two faces of the same problem it is natural to address them together. In this paper it is: (i) shown that correct modelling of the Bayer pattern in the generative process improves the super-resolution performance for colour images, and (ii) an algorithm that incorporates the two colour prior into the probabilistic model is designed. The algorithm presented in this paper focuses on the classes of images that have two dominant colours, i.e. most of the areas in the image are uniformly coloured. A convex optimization procedure for joint super-resolution and demosaicing is developed which outperforms state-of-the-art algorithms.     

Super-resolution pupil filtering for visual performance enhancement using adaptive optics

Ocular aberration correction can significantly improve visual function of the human eye. However, even under ideal aberration correction conditions, pupil diffraction restricts the resolution of retinal images. Pupil filtering is a simple super-resolution (SR) method that can overcome this diffraction barrier. In this study, a 145-element piezoelectric deformable mirror was used as a pupil phase filter because of its programmability and high fitting accuracy. Continuous phase-only filters were designed based on Zernike polynomial series and fitted through closed-loop adaptive optics. SR results were validated using double-pass point spread function images. Contrast sensitivity was further assessed to verify the SR effect on visual function. An F-test was conducted for nested models to statistically compare different CSFs. These results indicated CSFs for the proposed SR filter were significantly higher than the diffraction correction (p < 0.05). As such, the proposed filter design could provide useful guidance for supernormal vision optical correction of the human eye.     

Correction of dichroic mirror-induced PSF distortion in STED microscopy

Due to its spectral transmission/reflection properties, dichroic mirror (DM) is widely applied in nearly all microscopes to separate and combine beams with diverse wavelengths. However, its potential distortion effects to the point-spread function (PSF) have been ignored to a large extent. Here, we built a mathematical model to quantify these effects. Specifically, we focus on STED microscopy, whose performance is extremely sensitive to the systematic deficiencies. Both excitation and depletion beams are analysed, and the peak intensity and the shape of the effective PSF are calculated accordingly. More importantly, the strategy to compensate the DM-induced PSF distortion is also given in this paper.     

Single depth map super-resolution via joint non-local self-similarity modeling and local multi-directional gradient-guided regularization

The development of consumer depth cameras makes it possible to acquire depth information of a scene in real-time. However, low resolution and low quality of a depth map has greatly constrained its applications. In this paper, we propose a novel framework for single depth map super-resolution, which considers local and non-local information jointly in the depth map. For the non-local constraint, group-based sparse representation is used to explore non-local self-similarity in the depth map. For the local constraint, a multi-directional gradient-guided regularization is proposed to describe the gradient of the depth map with spatially varying orientations. The former constraint contains the visual artifacts effectively, while the latter restores sharp edge and fine structure. Finally, the two complementary regularizers are jointly casted into a unified optimization framework, where a split Bregman-based technique is developed to tackle the optimization problem. Both quantitative and qualitative evaluations indicate that the proposed method can obtain better reconstruction performance compared with state-of-the-art methods.     

基于单亲遗传算法和子像元/像元空间吸引模型的亚像元制图研究

混合像元普遍存在于遥感图像数据中。与传统的硬分类(Hard Classification)方法相比,在处理混合像元时,软分类(Soft Classification)技术可以避免信息丢失;但是,通过软分类技术获得的结果,仍然无法确定各分类在像元中的具体位置。子像元制图(或超分辨率制图、亚像元制图)技术能将软分类技术得到的结果转化为更高分辨率的图像,它能兼得软分类和硬分类两者的优势。将遗传算法的一个变种-单亲遗传算法应用于子像元制图,结合子像元/像元空间吸引模型,单亲遗传算法能直接获得子像元制图结果。以合成的图像和实际的土地覆盖图像为实验对象,通过目视比较和定量精度评价,与硬分类的结果相比,该方法能取得更高的制图精度和更好的结果。