Block-based Compressive Sensing Research Articles

Image Compression Based on Near Lossless Predictive Measurement Coding for Block-Based Compressive Sensing

Image Compression Based on Near Lossless Predictive Measurement Coding for Block-Based Compressive Sensing

Block-based compressive sensing in deep learning using AlexNet for vegetable classification

Vegetables can be distinguished according to differences in color, shape, and texture. The deep learning convolutional neural network (CNN) method is a technique that can be used to classify types of vegetables for various applications in agriculture. This study proposes a vegetable classification technique that uses the CNN AlexNet model and applies compressive sensing (CS) to reduce computing time and save storage space. In CS, discrete cosine transform (DCT) is applied for the sparsing process, Gaussian distribution for sampling, and orthogonal matching pursuit (OMP) for reconstruction. Simulation results on 600 images for four types of vegetables showed a maximum test accuracy of 98% for the AlexNet method, while the combined block-based CS using the AlexNet method produced a maximum accuracy of 96.66% with a compression ratio of 2×. Our results indicated that AlexNet CNN architecture and block-based CS in AlexNet can classify vegetable images better than previous methods.

Adaptive rate image compressive sensing based on the hybrid sparsity estimation model

In some actual image compressive sensing (CS) systems, the sparsity of the original image signals is unknown on the sampling side. Resources on the sampling side are also limited. Therefore, it is a challenging task to implement adaptive rate allocation on the sampling side of a CS system. In this paper, a novel adaptive rate allocation block CS scheme for images based on a hybrid sparsity estimation model is proposed. First, a cosine similarity sparsity estimation model is constructed by directly modeling the measurement results, and on the reconstruction side, a thresholding cosine transform sparsity estimation model is constructed according to the initial reconstructed image. Then, the sparsity estimates generated by the above two models are fused according to their respective characteristics using a Bayesian probability fusion algorithm. The advantage of the proposed scheme is that it can significantly improve the accuracy of the final sparsity estimates while limiting the computational complexity on the sampling side, which makes it exceptionally suitable for wireless sensor network applications. On the reconstruction side, we propose an energy-based adaptive global reconstruction model, that can further reduce block artifacts caused by block-based CS. The experiment results show that, compared with previous state-of-the-art schemes, the proposed scheme can achieve a significant improvement in the peak signal-to-noise ratio at the same sampling rate.

CS-LeCT: Chained Secure and Low-Energy Consumption Data Transmission Based on Compressive Sensing

Energy consumption and security are two major challenges faced by telemedicine systems. Due to the simplicity and the randomness in information acquisition process, compressive sensing (CS) has a vast prospect in dealing with these two problems. Recently, a chained CS (CCS) scheme has been proposed, which can implement a secure and low-energy consumption data transmission. However, due to its chain computation and the lossy compression nature of CS, there exists an accumulative error in the existing CCS scheme. Moreover, the block-based CS decoding also leads to a significant performance deterioration. In this paper, a new chained secure and low-energy consumption data transmission (CS-LeCT) scheme has been proposed, whose reconstruction performance is far superior to the CCS. Firstly, to overcome the accumulative error of the CCS scheme, square matrices instead of under-determined matrices were used to encode the signal so that in the proposed CS-LeCT the decoding process can be carried out by adopting a lossless inverse operation. Secondly, inspired by the structurally random matrix (SRM), an active packet drop strategy was designed to achieve low-energy consumption data transmission, whose decoding can be implemented by means of the SRM-based CS reconstruction. The performance gain of the CS-LeCT has not only been validated through simulation experiments but also been analyzed theoretically. Safety analysis also shows that the proposed CS-LeCT can resist several potential attacks, such as ciphertext-only attacks (COA), known-plaintext attacks (KPA) and man-in-the-middle attacks (MiTM).

BCS-AE: Integrated Image Compression-Encryption Model Based on AE and Block-CS

For Compressive Sensing problems, a number of techniques have been introduced, including traditional compressed-sensing (CS) image reconstruction and Deep Neural Network (DNN) models. Unfortunately, due to low sampling rates, the quality of image reconstruction is still poor. This paper proposes a lossy image compression model (i.e. BCS-AE), which combines two different types to produce a model that uses more high-quality low-bitrate CS reconstruction. Initially, block-based compressed sensing (BCS) was utilized, and it was done one block at a time by the same operator. It can correctly extract images with complex geometric configurations. Second, we create an AutoEncoder architecture to replace traditional transforms, and we train it with a rate-distortion loss function. The proposed model is trained and then tested on the CelebA and Kodak databases. According to the results, advanced deep learning-based and iterative optimization-based algorithms perform better in terms of compression ratio and reconstruction quality.

An efficient medical image compression technique for telemedicine systems

The medical practitioners primarily used medical images to reveal abnormalities in the internal critical organs and structures of body covered by the bones and the skin. Main application of medical imaging is to perform medical diagnosis from the image features extracted. Processing these images are very much required for assessing the patient’s condition. However, long-term monitoring of the patient using certain medical imaging technologies produces enormous volumes of data everyday. There is a need to compress the data to reduce redundancies and speed up the acquisition process, making them suitable for efficient transmission and analysis. Recently Compressed Sensing (CS) has been widely used for image compression at high speed with fewer samples. High-quality reconstruction using conventional CS and Block based CS (BCS) is a matter of utmost concern as they follow the random selection of samples. This could be overcome by adaptively selecting samples from various image regions using Adaptive Block Compressed Sensing (ABCS). This paper proposes Coefficient Mixed Thresholding based ABCS (CMT-ABCS) for compressing different medical images with a high compression ratio. The experimental outcomes exhibit a noteworthy improvement in the proposed method’s performance metrics when compared to other state-of-the-art approaches. There is a increase in PSNR of 5–10 dB, SSIM of 0.1–0.2 with NCC values closer to 1 and NAE values closer to 0. At low sampling rate, the reconstruction was greatly enhanced with only around 10% measurements/samples.

Perceptual Hashing With Complementary Color Wavelet Transform and Compressed Sensing for Reduced-Reference Image Quality Assessment

Image quality assessment (IQA) is an important task of image processing and has diverse applications, such as image super-resolution reconstruction, image transmission and monitoring systems. This paper proposes a perceptual hashing algorithm with complementary color wavelet transform (CCWT) and compressed sensing (CS) for reduced-reference (RR) IQA. The CCWT is exploited to decompose input color image into different sub-bands. Since the calculation of CCWT uses all color channels without discarding any information, the distortions introduced by digital operations on color channels are preserved in the CCWT sub-bands. The block-based CS is used to extract features from the CCWT sub-bands. As the Euclidean distance between the block-based CS features is slightly influenced by content-preserving operations, perceptual features constructed by Euclidean distances are robust, discriminative and compact. Hash sequence is finally determined by quantifying the perceptual features. Effectiveness of the proposed hashing is verified by various experiments on four open image databases. Experimental results demonstrate that the proposed hashing is superior to some state-of-the-art algorithms in terms of classification and RR IQA application.

Segment Based Compressive Sensing (SBCS) of Color Images for Internet of Multimedia Things Applications

Telemedicine is one of the IoMT applications transmitting medical images from hospital to remote medical centers for diagnosis and treatment. To share this multimedia content across internet, storage and transmission become a challenge because of its huge volume. New compression techniques are being continuously introduced to circumvent this issue. Compressive sensing (CS) is a new paradigm in signal compression. Block based compressive sensing (BCS) is a standard and commonly used technique in color image compression. However, BCS suffers from block artifacts and during transmission, mistakes can be introduced to affect the BCS coefficients, degrading the reconstructed image’s quality. The performance of BCS at low compression ratios is also poor. To overcome these limitations, without dividing the image into blocks, the image matrix is considered as a whole and compressively sensed by segment based compressive sensing (SBCS). This is a novel strategy that is offered in this article, for efficient compression of digital color images at low compression ratios. Metrics of performance The peak signal to noise ratio (PSNR), the mean structural similarity index (MSSIM), and the colour perception metric delta E are computed and compared to those obtained using block-based compressive sensing (BBCS). The results show that SBCS performs better than BBCS.

Block-Based Compressed Sensing for Fast Optic Fiber Bundle Imaging with High Spatial Resolution

Block-Based Compressed Sensing for Fast Optic Fiber Bundle Imaging with High Spatial Resolution

Block compressed sensing image reconstruction via deep learning with smoothed projected Landweber

Compressed sensing (CS) is a signal processing framework for effectively reconstructing signal from a small number of measurements obtained by linear projections of the signal. It is an ongoing challenge for the real-time image reconstruction of the computational imaging, including single pixel imaging based on CS. We built a block-based CS (BCS) image reconstruction framework via a deep learning network with smoothed projected Landweber (SPL). A fully connected network performs both BCS linear sensing and non-linear reconstruction stages, and SPL removes the blocking artifacts due to incorporate Wiener filtering into projected Landweber (PL) method at each iteration. The sensing matrix and nonlinear prediction operator are jointly optimized, and the smoothing filtering is coalesced into the PL framework for eliminating high-frequency oscillatory blocking artifact. Experimental results reveal that the optimized scheme outperforms the approach only based on deep neural network. The reconstruction quality can be improved while being only slightly slower, especially the gain of structural similarity is significantly better than peak signal-to-noise ratio, and the reconstruction image texture details are vivid and natural. At 10% sensing rate, the structural similarity maximum (minimum) gain reaches 0.098 (0.021). The proposed approach is not only far superior to other state-of-the-art CS algorithms in terms of reconstruction time and quality but also comparable with up-to-date deep learning methods.

Recovery of image and video based on compressive sensing via tensor approximation and Spatio-temporal correlation

In recent years, block-based compressive sensing (BCS) has been extensively studied because it can reduce computational complexity and data storage by dividing the image into smaller patches, but the performance of the reconstruction algorithm is not satisfactory. In this paper, a new reconstruction model for image and video is proposed. The model makes full use of spatio-temporal correlation and utilizes low-rank tensor approximation to improve the quality of the reconstructed image and video. For image recovery, the proposed model obtains a low-rank approximation of a tensor formed by non-local similar patches, and improves the reconstruction quality from a spatial perspective by combining non-local similarity and low-rank property. For video recovery, the reconstruction process is divided into two phases. In the first phase, each frame of the video sequence is regarded as an independent image to be reconstructed by taking advantage of spatial property. The second phase performs tensor approximation through searching similar patches within frames near the target frame, to achieve reconstruction by putting the spatio-temporal correlation into full play. The resulting model is solved by an efficient Alternating Direction Method of Multipliers (ADMM) algorithm. A series of experiments show that the quality of the proposed model is comparable to the current state-of-the-art recovery methods.

An enhanced block-based Compressed Sensing technique using orthogonal matching pursuit

The theory of compressed sensing asserts that one can recover signals in $$\mathbb {R}^n$$ from far fewer samples or measurements, if the signal has a sparse representation in some orthonormal basis; from non-adaptive linear measurements by solving a $$\mathbb {L}_1$$ norm minimisation problem. The non-adaptive measurements have the character of random linear combinations of the basis or frame elements. However, for large-scale 2D image signals, the randomized sensing matrix consumes enormous computational resources that makes it impractical. The problem has been addressed in the paper as a block compressed sensing (BCS) with sparsity normalization in the transformed domain in the preprocessing stage. The blocks obtained are converted to non-adaptive measurements using identically independent weighted Gaussian random matrices. The feasibility of reconstruction is verified using orthogonal matching pursuit. Simulation results show that better reconstruction performance can be achieved by the proposed technique in comparison with the existing BCS approaches.

IPiano-Net: Nonconvex optimization inspired multi-scale reconstruction network for compressed sensing

Compressed sensing (CS) aims to precisely reconstruct the original signal from under-sampled measurements, which is a typical ill-posed problem. Solving such a problem is challenging and generally needs to incorporate suitable priors about the underlying signals. Traditionally, these priors are hand-crafted and the corresponding approaches generally have limitations in expressive capacity. In this paper, a nonconvex optimization inspired multi-scale reconstruction network is developed for block-based CS, abbreviated as iPiano-Net, by unfolding the classic iPiano algorithm. In iPiano-Net, a block-wise inertial gradient descent interleaves with an image-level network-inducing proximal mapping to exploit the local block and global content information alternately. Therein, network-inducing proximal operators can be adaptively learned in each module, which can efficiently characterize image priors and improve the modeling capacity of iPiano-Net. Such learned image-level priors can suppress blocky artifacts and noises/corruptions while preserving the global information. Different from existing discriminative CS reconstruction models trained with specific measurement ratios, an effective single model is learned to handle CS reconstruction with several measurement ratios even the unseen ones. Experimental results demonstrate that the proposed approach is substantially superior to previous CS methods in terms of Peak Signal to Noise Ratio (PSNR) and visual quality, especially at low measurement ratios. Meanwhile, it is robust to noise while maintaining comparable execution speed.

A Compressive Sensing CMOS Image Sensor With Partition Sampling Technique

A CMOS image sensor (CIS) that performs compressive sensing (CS) image encoding without compromising the operating speed and hardware complexity is presented in this article. The conversion rate and the frame rate of the sensor are increased by using a high-order sigma-delta (ΣΔ) analog-to-digital converter (ADC) to obtain linear measurements of selected pixel values for CS encoding. Image distortion caused by the nonconstant weight function of high-order ΣΔ ADCs is eliminated by using the proposed sampling technique. The sampling technique makes the effective weights of the inputs to the ADC equal by applying a solution for the set partition problem. In addition, unlike the existing block-based CS encoding scheme that requires complicated analog multiplexers, the proposed column-based CS encoding scheme can be implemented in a hardware-efficient column-parallel fashion, as in conventional CISs. The CIS is fabricated in a 0.11-μm 1P4M CIS process, and the sampling technique and encoding scheme are successfully demonstrated. It achieves a readout noise of 2.63 e <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-</sup> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rms</sub> and a dynamic range of 67.96 dB with a power consumption of 56.38 mW. The resulting figure of merit is 0.375 e <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-</sup> ·nJ, which is the lowest among those of recently reported state-of-the-art CS-CISs.

Soft Video Multicasting Using Adaptive Compressed Sensing

Recently, soft video multicasting has gained a lot of attention, especially in broadcast and mobile scenarios where the bit rate supported by the channel may differ across receivers, and may vary quickly over time. Unlike the conventional designs that force the source to use a single bit rate according to the receiver with the worst channel quality, soft video delivery schemes transmit the video such that the video quality at each receiver is commensurate with its specific instantaneous channel quality. In this paper, we present a soft video multicasting system using an adaptive block-based compressed sensing (BCS) method. The proposed system consists of an encoder, a transmission system, and a decoder. At the encoder side, each block in each frame of the input video is adaptively sampled with a rate that depends on the texture complexity and visual saliency of the block. The obtained BCS samples are then placed into several packets, and the packets are transmitted via a channel-aware OFDM (orthogonal frequency division multiplexing) transmission system with a number of subchannels. At the decoder side, the received BCS samples are first used to build an initial approximation of the transmitted frame. To further improve the reconstruction quality, an iterative BCS reconstruction algorithm is then proposed that uses an adaptive transform and an adaptive soft-thresholding operator, which exploits the temporal similarity between adjacent frames to achieve better reconstruction quality. The extensive objective and subjective experimental results indicate the superiority of the proposed system over the state-of-the-art soft video multicasting systems.

Self-adaptive block-based compressed sensing imaging for remote sensing applications

In order to effectively alleviate the pressure of high-resolution imaging and massive data storage and transmission, it is of great practical significance to introduce compressed sensing into remote sensing applications. From the perspective of imaging control strategy, the typical block-based compressed sensing (BCS) system is optimized. Based on the fact that there are generally significant differences between regions of remote sensing images, a self-adaptive BCS method is proposed. Compared with the traditional BCS system, the prior information of the imaging target is obtained first by adding a presampling process. On the one hand, it is used to generate a saliency information map, which guides the reasonable allocation of self-adaptive sampling ratios between blocks in the compressed sampling process, thereby improving the sampling efficiency. On the other hand, it is used to generate the weighted sparse coefficient matrix, which will be substituted into the theoretical model in the image restoration process, thus improving the image restoration efficiency. The experimental results show that the imaging quality of the proposed method has a significant improvement compared with the traditional system and is also superior to several existing self-adaptive methods. In addition, on the basis of the above method, a multiangle image restoration strategy is proposed, which further improves the image quality at the cost of four times the image restoration time.

Multi-Channel Deep Networks for Block-Based Image Compressive Sensing

Incorporating deep neural networks in image compressive sensing (CS) receives intensive attentions in multimedia technology and applications recently. As deep network approaches learn the inverse mapping directly from the CS measurements, the reconstruction speed is significantly faster than the conventional CS algorithms. However, for existing network based approaches, a CS sampling procedure has to map a separate network model. This may potentially degrade the performance of image CS with block-wise sampling because of blocking artifacts, especially when multiple sampling rates are assigned to different blocks within an image. In this paper, we develop a multichannel deep network for block-based image CS by exploiting inter-block correlation with performance significantly exceeding the current state-of-the-art methods. The significant performance improvement is attributed to block-wise approximation but full image removal of blocking artifacts. Specifically, with our multichannel structure, the image blocks with a variety of sampling rates can be reconstructed in a single model. The initially reconstructed blocks are then capable of being reassembled into a full image to improve the recovered images by unrolling a hand-designed block based CS recovery algorithm. Experimental results demonstrate that the proposed method outperforms the state-of-the-art CS methods by a large margin in terms of objective metrics and subjective visual image quality. Our source codes are available at https://github.com/siwangzhou/DeepBCS.

A Two-Branch Convolution Residual Network for Image Compressive Sensing

Deep learning has made great progress in image compressive sensing (CS) tasks recently, and several CS models based on it have achieved superior performance. In practice, sensing the entire image requires huge memory and computational effort. Although the block-based CS method can effectively realize image sensing, it will cause block effects that severely decrease the reconstruction performance. To this end, this paper proposes a two-branch convolution residual network for image compressive sensing (denoted as TCR-CS), which mainly consists of a two-branch convolution autoencoder network and a residual network. Specifically, the two-branch convolution autoencoder network senses the entire image through multiple scale convolutional filters to obtain measurements. For better CS reconstruction, the image is preliminarily reconstructed by the deconvolution decoder network, and then the residual network is used to optimize the pre-reconstructed image. Through the end-to-end training, all networks can be jointly optimized. Finally, experimental results demonstrate that the proposed TCR-CS method is superior to existing state-of-the-art CS methods in terms of structural similarity, reconstruction performance and visual quality at different measurement rates.

Compressed Pseudo-Analog Transmission System for Remote Sensing Images Over Bandwidth-Constrained Wireless Channels

Recently, pseudo-analog transmission based on SoftCast has been proposed to improve the received quality of video/image by eliminating the cliff effect in traditional digital transmission. In this paper, we propose a Compressed Pseudo-analog Transmission System (ComPaTS) for remote sensing images over bandwidth-constrained wireless channels. This novel scheme is developed based on the observation that the inherent dropping strategy in pseudo-analog transmission is impractical for remote sensing images in which the transmission bandwidth is generally insufficient. In ComPaTS, to guarantee the content diversity gain under pseudo-analog transmission, block-based Compressive Sensing (CS) is applied to the wavelet domain of each remote sensing image, where the sampling ratio is proportional to the importance of different blocks. The main work of ComPaTS is to leverage the sampling ratio in block-based CS and the resource allocation in pseudo-analog transmission in order to minimize system distortion. Two components of system distortion, i.e., source distortion and channel distortion are analyzed respectively. To characterize the coupling relationship between these two different types of distortion, a joint bandwidth-power distortion optimization problem is formulated. Furthermore, we also propose a two-stage allocation algorithm to solve the problem efficiently. The simulation results demonstrate that the proposed ComPaTS scheme significantly outperforms reference schemes in terms of peak signal-to-noise ratio under different bandwidth-constrained scenarios.

Reconstruction of undersampled atomic force microscope images using block-based compressive sensing

Atomic force microscope (AFM) is a high-precision instrument to research surface topography of various samples. Nevertheless, the standard AFM procedure takes excessively long time to acquire high-resolution images. Moreover, too much interaction between a probe tip and a specimen potentially leads to tip abrasion and sample damage. Compressive sensing (CS) has been employed to realize high-speed AFM (HS-AFM). However, a considerably large-sized image may be hard to be reconstructed through the common CS method with some ordinary algorithms, due to the high computational complexity and hardware costs. Thus, block-based compressive sensing (BCS) is adopted as an alternative means. In our work, three scanning patterns were utilized to generate undersampled AFM images at various sampling rates for five samples with different surface appearance. Each image block was reconstructed through one of six representative algorithms. Finally, an optimal measurement scheme for BCS-HS-AFM was put forward based on the analysis of experimental results, which consisted of sampling rate, scanning pattern, block mode and reconstruction algorithm. BCS was compared with other CS methods in terms of the computational complexity and image reconstruction effect to reveal its superior performance. In brief, BCS can be applied to effectively realize the high-resolution and low-damage HS-AFM imaging.