Bayesian sparse hierarchical model for image denoising

Sparse models and their variants have been extensively investigated, and have achieved great success in image denoising. Compared with recently proposed deep-learning-based methods, sparse models have several advantages: (1) Sparse models do not require a large number of pairs of noisy images and the corresponding clean images for training. (2) The performance of sparse models is less reliant on the training data, and the learned model can be easily generalized to natural images across different noise domains. In sparse models, ${\ell }_0$ norm penalty makes the problem highly non-convex, which is difficult to be solved. Instead, ${\ell }_1$ norm penalty is commonly adopted for convex relaxation, which is considered as the Laplacian prior from the Bayesian perspective. However, many previous works have revealed that ${\ell }_1$ norm regularization causes a biased estimation for the sparse code, especially for high-dimensional data, e.g., images. In this paper, instead of using the ${\ell }_1$ norm penalty, we employ an improper prior in the sparse model and formulate a hierarchical sparse model for image denoising. Compared with other competitive methods, experiment results show that our proposed method achieves a better generalization for images with different characteristics across various domains, and achieves state-of-the-art performance for image denoising on several benchmark datasets.     

基于联合低秩稀疏分解的红外与可见光图像融合

为进一步提高红外与可见光融合图像的细节信息和整体对比度,降低伪影和噪声,考虑了红外与可见光图像的相关性,提出了一种基于联合低秩稀疏分解的红外与可见光图像融合方法。首先,利用联合低秩稀疏分解方法将红外和可见光源图像分别分解成共同低秩分量、特有低秩分量和特有稀疏分量;其次,利用非下采样Shearlet变换方法对特有低秩分量进行融合;然后,采用区域能量融合策略实现特有稀疏分量融合;最后,共有低秩分量与融合后的特有低秩分量和特有稀疏分量相加得到最终融合图像。在Nato-camp、Bristol Eden Project和TNO公共测试数据集上进行的实验测试了所提算法性能。实验结果表明,与其他9种融合方法相比,所提方法能够有效地提取红外图像中的目标信息和保留可见光图像的背景信息,熵、互信息、标准差、视觉信息保真度、差异相关系数之和和 Qy 客观评价指标明显优于对比方法。      

利用结构化和一致性约束的稀疏表示模型进行红外和可见光图像融合

红外和可见光图像融合作为图像融合技术中一个重要组成部分,被广泛应用于军事、工业和生活领域。它能够集成两种模态图像的互补信息,融合成一幅信息丰富、质量较好的图像,不仅能够突出目标信息,还能够保持源有图像的纹理信息和一些显著性的细节。本文提出一种新的红外和可见光图像融合方法,在鲁棒稀疏表示模型的基础上增加了结构化稀疏约束,同时结合了图像区域特征相似的一致性约束项,克服现有一些方法所存在的局部模糊和纹理细节丢失等问题,提高了图像融合的精度。本文主要构建了结构化稀疏表示与一致性约束模型,将其应用到红外和可见光图像融合中并进行了求解,将源图像分解为背景信息和显著性信息,再对背景和显著性信息分别设计融合规则,最后利用字典进行重构,获得红外和可见光融合后的图像。实验结果表明,本文提出的融合算法优于现有的一些多聚焦图像融合算法。      

Multi-modality medical image fusion based on separable dictionary learning and Gabor filtering

Sparse representation (SR) has been widely used in image fusion in recent years. However, source image, segmented into vectors, reduces correlation and structural information of texture with conventional SR methods, and extracting texture with the sliding window technology is more likely to cause spatial inconsistency in flat regions of multi-modality medical fusion image. To solve these problems, a novel fusion method that combines separable dictionary optimization with Gabor filter in non-subsampled contourlet transform (NSCT) domain is proposed. Firstly, source images are decomposed into high frequency (HF) and low frequency (LF) components by NSCT. Then the HF components are reconstructed sparsely by separable dictionaries with iterative updating sparse coding and dictionary training. In the process, sparse coefficients and separable dictionaries are updated by orthogonal matching pursuit (OMP) and manifold-based conjugate gradient method, respectively. Meanwhile, the Gabor energy as weighting factor is utilized to guide the LF components fusion, and this further improves the fusion degree of low-significant feature in the flat regions. Finally, the fusion components are transformed to obtain fusion image by inverse NSCT. Experimental results demonstrate the more competitive results of the proposal, leading to the state-of-art performance on both visual quality and objective assessment.     

结合潜在低秩分解和稀疏表示的脑部图像融合

针对低秩分解和稀疏表示(space representation,SR) 造成融合图像信息缺失的问题,提出一种结合潜在低秩分解和SR的脑部图像融合算法。首先,将源图像分解为低秩、稀疏和噪声3种成分,面对不同分解成分特性间的差异,分别构造低秩字典和稀疏字典进行描述:采用加权灰度值的方法处理低秩成分,以保持其轮廓和亮度特征;对于稀疏成分,设计一种多范数加权度量的方法对SR进行改进,以保持其高维信息,剔除噪声成分。比对当前主流的5种算法,在视觉效果和客观指标上,本文方法效果最优。     

基于稀疏分解的指静脉图像去噪

手指静脉识别技术因其独特的优势,受到广泛的关注。然而由硬件系统获取的手指静脉图像常常含有严重的噪声、阴影等问题,所以对低质量的静脉图像的去噪成为了整个识别过程的关键。本文提出了一种基于稀疏分解的指静脉图像去噪新方法。基于稀疏分解的图像去噪是将含有噪声的图像信息进行稀疏分解,分解成稀疏成分和其他成分。其中的稀疏部分是有用信息,其他部分被认为是噪声,再由图像的稀疏部分重建原始信号,达到恢复原始信号并去除噪声的效果。本文根据指静脉图像的静脉的特点,应用高斯函数构造了过完备库。用合成图像和真实指静脉图像分别对新算法进行实验验证。实验结果证明,与传统的去噪算法相比,峰值信噪比提高1-2dB。      

Image deblocking via shape-adaptive low-rank prior and sparsity-based detail enhancement

At low bit rates, visually annoying blocking artifacts are usually introduced in JPEG compressed images. In this paper, we proposed an image deblocking method combined with the shape-adaptive low-rank (SALR) prior, the quantization constraint (QC) prior and sparsity-based detail enhancement. We firstly design a deblocking model to obtain initial deblocked images under the maximum a posteriori (MAP) framework. More specifically, with the assumption of Gaussian quantization noise, the SALR prior is utilized to effectively separate signal from noise and preserve image edges. Compared with previous low rank priors, the SALR reconstructs a better result via shape adaptive blocks. The QC prior is also adopted to avoid over-smoothing and to enable a more accurate estimation. Finally, by extracting features of external images, the mapping matrix of sparse dictionary pairs is trained to enhance image details. Extensive experimental results demonstrate that the proposed deblocking method has superior performances in both subjective vision and objective quality.     

Multitone reconstruction visual cryptography based on phase periodicity

The current no-computation grayscale image visual cryptography (VC) can only achieve halftone reconstruction but cannot truly achieve multitone. To solve this problem, we propose the concept of phase periodicity of the $\lambda /2$ retarder film and calculate the optical axis angle set with phase periodicity. According to the phase periodicity, we propose a $\lambda /2$ retarder film phase periodicity visual cryptography (RPP-VC). In RPP-VC, the secret pixels are encoded as the optical axis angles of $n$ $\lambda /2$ retarder films and distributed to $n$ shares. The decoding process does not require computation. The reconstructed image has no pixel expansion and can reach up to 23 tones. The quality of the reconstructed images has been greatly improved and the evaluation indicators of perceived quality are nearly doubled compared with other grayscale image VC schemes. The experimental results verify the feasibility of RPP-VC.