3D Transform-domain Research Articles

Image Inpainting by Low-Rank Prior and Iterative Denoising

To reconstruct the missing or damaged parts of images from observed incomplete data, some traditional methods have been researched in recent years. The iterative denoising and backward projections(IDBP)algorithm with a simple parameter mechanism have been recently introduced, which solves the typical inverse problem by utilizing the existing 3D transform-domain collaborative filtering denoising algorithm(BM3D). While this algorithm has simple parameter tuning, the collaborative hard-thresholding applied to the 3D group is greatly restricted in the procedure of denoising. In this paper, we remedy this deficiency using an iteration reweighted shrinkage denoising method. First, the model is obtained by a Plug and Play(P&P) framework. Then, we solve the optimization problem by using a proposed denoising model based on low rank prior and reweighted shrinkage and obtain a closed-form solution. Finally, the closed-form solution is operated iteratively by using the adaptive backward projection technique. Utilizing this novel strategy, the proposed algorithm not only removes the image noise and effectively recovers the degraded image, but also preserves fine structure and texture information of the image. Experimental results indicate that the proposed algorithm is competitive with some state-of-the-art inpainting algorithms in terms of both numerical evaluation and visual quality.

Manhattan Distance-Based Adaptive 3D Transform-Domain Collaborative Filtering for Laser Speckle Imaging of Blood Flow.

Laser speckle contrast imaging (LSCI) is a full-field, noncontact imaging technology for mapping blood flow with high spatio-temporal resolution, in which the speckle contrast can be estimated either in spatial domain or temporal domain. Temporal LSCI (tLSCI) provides higher spatial resolution than spatial domain does. However, when the number of sampling frames is limited, it is difficult to obtain accurate blood flow velocity owing to the significant statistical noise. The widely used spatially averaged tLSCI (savg-tLSCI) usually requires a large number of sampling frames to obtain acceptable denoising performance. Here, based on the nonlocal filtering strategy of block-matching and three-dimensional transform-domain collaborative filtering (BM3D), Manhattan distance-based adaptive BM3D (MD-ABM3D) is proposed to effectively manage the complicated inhomogeneous noise in tLSCI image and improve the signal-to-noise ratio. Manhattan distance improves the accuracy of the block matching in strong noise, and the adaptive algorithm adapts to the inhomogeneous noise and estimates suitable parameters for improved denoising. MD-ABM3D improves 4.91 dB in peak signal-to-noise ratio relative to savg-tLSCI. It achieves stability for denoising tLSCI image with different temporal windows. The image-quality evaluation of MD-ABM3D for tLSCI (t = 20 frames) equals that of savg-tLSCI (t = 60 frames). It achieves high signal-to-noise ratio with a reduced number of sampling frames. A reduced number of sampling frames are more practical for biomedical applications. It also offers higher temporal resolution and less disturbance from the motion of the moving object.

Improved Sparse 3D Transform-Domain Collaborative Filter for Screen Content Image Denoising

Screen content videos or images are widely used in applications such as screen sharing. Compressed screen content videos or images may have distortions or noises because of the quantization process. This paper proposes an improved sparse 3D transform-domain collaborative filter to enhance screen content image quality by block classification and block segmentation. Experimental results show that the proposed algorithm achieves a peak signal-to-noise ratio increase and subjective visual quality improvements for reconstructed screen content images.

Image restoration approach using a joint sparse representation in 3D-transform domain

Image restoration is a crucial problem in image processing and a necessary step before the image segmentation and recognition. A new framework for image restoration in 3D transform domain terms as joint sparse representation (JSR) is proposed in this work. The proposed JSR is able to represent image more sparsely and more precisely in the transform domain by performing 3D transform on each set of similar blocks. In addition to that, in order to overcome the issues of defective block matching and spurious artifact in the 3D sparse representation, JSR introduces a new nonlocal regularization term which characterizes the statistics of the nonlocal image to improve the accuracy of the estimated coefficients. The parameters of regularization terms are calculated based on Bayesian philosophy, and a split Bregman-based technique is developed to obtain the solution in a tractable and robust manner. Extensive experiments on image denoising, image inpainting and image deblurring demonstrate that the proposed JSR algorithm outperforms current state-of-the-art approaches in terms of peak signal-to-noise ratio and visual quality.

Spatio-Temporal Denoising for Depth Map Sequences

This paper proposes a novel strategy for depth video denoising in RGBD camera systems. Depth map sequences obtained by state-of-the-art Time-of-Flight sensors suffer from high temporal noise. Hence, all high-level RGB video renderings based on the accompanied depth maps' 3D geometry like augmented reality applications will have severe temporal flickering artifacts. The authors approached this limitation by decoupling depth map upscaling from the temporal denoising step. Thereby, denoising is processed on raw pixels including uncorrelated pixel-wise noise distributions. The authors' denoising methodology utilizes joint sparse 3D transform-domain collaborative filtering. Therein, they extract RGB texture information to yield a more stable and accurate highly sparse 3D depth block representation for the consecutive shrinkage operation. They show the effectiveness of our method on real RGBD camera data and on a publicly available synthetic data set. The evaluation reveals that the authors' method is superior to state-of-the-art methods. Their method delivers flicker-free depth video streams for future applications.