Khanh Quoc Dinh

Measurement coding for compressive imaging using a structural measuremnet matrix

Compressive imaging can acquire image signal in an under-sampled (i.e., under Nyquist rate) representation called measurement. However, measurement compression still has an essential problem in its overall rate-distortion performance. In this paper, we propose a measurement prediction method in which the best predictor is directionally selected in order to reduce the entropy of measurement to be sent. Generally, the measurement prediction usually works well with a small block while the quality of recovery is known to be better with a large block. In order to overcome this dilemma, we propose to use a structural measurement matrix with which compressive sensing is done in a small block size but recovery is performed in a large block size. In this way, both prediction and recovery are expected to be improved at the same time. Experimental results show its superiority in measurement coding amounting up to bitrate reduction by 39 %.

Multi-scale/multi-resolution Kronecker compressive imaging.

As a universal sampling procedure, compressive sensing (CS) considers that all samples of compressible signal are equally important. However, it is not true in in image/video signal since human visual system is more sensitive to low frequency components. Therefore, CS theory has been extended to hybrid and multi-scale CS to better capture the low-frequency samples. The computational complexity is another challenge in CS which can be solved by multi-resolution sensing matrix. In this paper, we propose a multi-scale/multi-resolution sensing matrix for Kronecker CS (KCS) based on separable wavelet transform and address measurement allocation problem with and without information of to-be-sensed image. The proposed methods not only perform better (3.72dB gain) but also low complexity and compatible with conventional reconstruction methods.

Deblocking filter for artifact reduction in distributed compressive video sensing

The distributed compressive video sensing (DCVS) poses itself as a very promising framework for future video coding on mobile devices due to its very low complexity at the encoder not only in sampling but also in compression. However its blocking artifacts and oscillatory artifacts especially in high-frequency components seriously degrade perceptual quality. In this paper, we try to solve both problems of the high-frequency oscillatory and blocking artifacts as a whole by a proposed deblocking filter. By differentiating those well-decoded blocks from poorly-decoded blocks using concept of reliability, the proposed method can improve DCVS framework in both objective and subjective qualities.

Edge-preserving block compressive sensing with projected landweber

Compressive Sensing (CS) is an emerging new sampling technique which helps to break through the Nyquist sampling frequency for sparse signals. This paper addresses improving one of its recovery algorithms known as the Block Compressive Sensing with Smooth Projected Landweber (BCS-SPL). For reducing the blocking artifacts in BCS-SPL, the Wiener filter has been implemented as a classic way to smooth image at the beginning of each iteration, but it is quite sensitive to image edges and blurs the image. In this paper, we propose a modified method which separates image signal into its low and high frequency components, and then independently processes each of the two components. Subsequently, a smoothness enhancing operation is implemented to improve reduction of high frequency oscillatory artifacts after hard thresholding. Experimental results show that the proposed method improves reconstructed image quality by more than 3dB compared to the conventional BCS-SPL.

Detail-preserving compressive sensing recovery based on cartoon texture image decomposition

In this paper, we propose a detail-preserving reconstruction method for total variation-based recovery in low subrate compressive sensing using cartoon texture image decomposition and residual reconstruction. It iteratively decomposes and reconstructs cartoon and texture image components separately. A nonlocal structure-preserving filter is utilized to reduce staircase artifacts while preserving nonlocal structures of image in the spatial domain. Experimental results show that the proposed method outperforms the conventional ones in terms of preserving small scale details of image.

Measurement Coding for Compressive Sensing of Color Images

From the perspective of reducing the sampling cost of color images at high resolution, block-based compressive sensing (CS) has attracted considerable attention as a promising alternative to conventional Nyquist/Shannon sampling. On the other hand, for storing/transmitting applications, CS requires a very efficient way of representing the measurement data in terms of data volume. This paper addresses this problem by developing a measurement-coding method with the proposed customized Huffman coding. In addition, by noting the difference in visual importance between the luma and chroma channels, this paper proposes measurement coding in YCbCr space rather than in conventional RGB color space for better rate allocation. Furthermore, as the proper use of the image property in pursuing smoothness improves the CS recovery, this paper proposes the integration of a low pass filter to the CS recovery of color images, which is the block-based l 20 -norm minimization. The proposed coding scheme shows considerable gain compared to conventional measurement coding.

Iterative Weighted Recovery for Block-Based Compressive Sensing of Image/Video at a Low Subrate

In compressive sensing (CS) of images or videos, a block-based sensing or recovery scheme can facilitate low-cost sampling or recovery in memory and computation. However, its recovery with small block size and small subrate suffers greatly from its lack of information of the measurement data essential to recover a unique solution among many candidates. This study, based on prior knowledge of the signal to be sensed, namely, the relative magnitude difference of signal entries, designs a weighting process to limit the solution space of the recovered signal and combines it with much simplified Landweber iterations to deliver a complete recovery algorithm, called iterative weighted recovery (IWR). We theoretically verify the performance of the proposed IWR, including error bound, convergence rate, and stopping criterion. Application of the proposed IWR to block-based CS of images or videos confirms the quality improvement of the recovered images or videos and reduction of recovery time.

Compressive sensing reconstruction via decomposition

When recovering images from a small number of Compressive Sensing (CS) measurements, a problem arises whereby image features (e.g., smoothness, edges, textures) cannot be preserved well in reconstruction, especially textures at small-scale. Since the missing information still remains in the residual measurement, we propose a novel Decomposition-based CS-recovery framework (DCR) which utilizes residual reconstruction and state-of-the-art filters. The proposed method iteratively refines residual measurement which is closely related to the denoise-boosting techniques. DCR is further incorporated with a weighted total variation and nonlocal structures in the gradient domain as priors to form the proposed Decomposition based Texture preserving Reconstruction (DETER). We subsequently demonstrate robustness of the proposed framework to noise and its superiority over the other state-of-the-art methods, especially at low subrates. Its fast implementation based on the split Bregman technique is also presented. HighlightsImage features (edge, smooth, texture regions) have different characteristics.A decomposition based CS reconstruction is proposed via denoising filters.An instance of image denoise boosting techniques in CS reconstruction.The better recovery and filter methods, the higher performance is archived.Multiple image priors improve the final CS reconstruction performance.

Edge-preserving nonlocal weighting scheme for total variation based compressive sensing recovery

Although total variation minimization technique is being widely used in compressive sensing recovery, it still suffers from the so called staircase artifact which is caused by losing fine details of image. As a solution for the problem, in this paper, we propose an edge-preserving weighting scheme utilizing nonlocal structure and histogram of natural image in the gradient domain. Experimental results show that the proposed scheme surpasses the traditional total variation and the edge-guided CS in both objective and subjective qualities.

Block compressive sensing of image and video with nonlocal Lagrangian multiplier and patch-based sparse representation

Although block compressive sensing (BCS) makes it tractable to sense large-sized images and video, its recovery performance has yet to be significantly improved because its recovered images or video usually suffer from blurred edges, loss of details, and high-frequency oscillatory artifacts, especially at a low subrate. This paper addresses these problems by designing a modified total variation technique that employs multi-block gradient processing, a denoised Lagrangian multiplier, and patch-based sparse representation. In the case of video, the proposed recovery method is able to exploit both spatial and temporal similarities. Simulation results confirm the improved performance of the proposed method for compressive sensing of images and video in terms of both objective and subjective qualities. HighlightsMulti-block gradient to reduce blocking artifacts by block-independent TV recovery.Lagrangian multiplier denoised directly by the nonlocal means filter.Low contrast details enhanced by patch-based sparse representation.Global and local sparsifying transforms to enhance sparsity level of noisy data.Extension to a block compressive video sensing problem (DCVS).