Peak Signal-to-noise Ratio Improvement Research Articles

Image deconvolution using hybrid threshold based on modified L1-clipped penalty in EM framework

Image deconvolution remains a challenging task due to its inherent ill-posedness. While existing algorithms show strong numerical performance, their complexity often complicates analysis and implementation. This paper introduces a computationally efficient image deconvolution method within the expectation maximization (EM) framework. The proposed algorithm alternates between an E-step leveraging the fast Fourier transform (FFT) and an M-step utilizing the discrete wavelet transform (DWT). In the M-step, we introduce a novel L1-clipped penalty to compute the maximum a posteriori (MAP) estimate, resulting in a hybrid threshold that combines the strengths of soft and hard thresholding. This hybrid threshold is mathematically derived, overcoming the high variance of hard-thresholding and the high bias of soft-thresholding, thus optimizing the trade-off between variance and bias. Extensive experiments demonstrate that our method significantly outperforms state-of-the-art techniques in terms of improved signal-to-noise ratio (ISNR) and peak signal-to-noise ratio (PSNR), as well as visual quality. Notably, the proposed method shows average PSNR improvements of 3.49 dB, 4.23 dB, and 1.44 dB for uniform blur and 0.76 dB, 3.57 dB, and 0.66 dB for Gaussian blur on the Set12, BSD68, and Set14 datasets, respectively.

Efficient Haze Removal from a Single Image Using a DCP-Based Lightweight U-Net Neural Network Model.

In this paper, we propose a lightweight U-net architecture neural network model based on Dark Channel Prior (DCP) for efficient haze (fog) removal with a single input. The existing DCP requires high computational complexity in its operation. These computations are challenging to accelerate, and the problem is exacerbated when dealing with high-resolution images (videos), making it very difficult to apply to general-purpose applications. Our proposed model addresses this issue by employing a two-stage neural network structure, replacing the computationally complex operations of the conventional DCP with easily accelerated convolution operations to achieve high-quality fog removal. Furthermore, our proposed model is designed with an intuitive structure using a relatively small number of parameters (2M), utilizing resources efficiently. These features demonstrate the effectiveness and efficiency of the proposed model for fog removal. The experimental results show that the proposed neural network model achieves an average Peak Signal-to-Noise Ratio (PSNR) of 26.65 dB and a Structural Similarity Index Measure (SSIM) of 0.88, indicating an improvement in the average PSNR of 11.5 dB and in SSIM of 0.22 compared to the conventional DCP. This shows that the proposed neural network achieves comparable results to CNN-based neural networks that have achieved SOTA-class performance, despite its intuitive structure with a relatively small number of parameters.

Synthetic Aperture Radar Radio Frequency Interference Suppression Method Based on Fusing Segmentation and Inpainting Networks

Synthetic Aperture Radar (SAR) is a high-resolution imaging sensor commonly mounted on platforms such as airplanes and satellites for widespread use. In complex electromagnetic environments, radio frequency interference (RFI) severely degrades the quality of SAR images due to its widely varying bandwidth and numerous unknown emission sources. Although traditional deep learning-based methods have achieved remarkable results by directly processing SAR images as visual ones, there is still considerable room for improvement in their performance due to the wide coverage and high intensity of RFI. To address these issues, this paper proposes the fusion of segmentation and inpainting networks (FuSINet) to suppress SAR RFI in the time-frequency domain. Firstly, to weaken the dominance of RFI in SAR images caused by high-intensity interference, a simple CCN-based network is employed to learn and segment the RFI. This results in the removal of most of the original interference, leaving blanks that allow the targets to regain dominance in the overall image. Secondly, considering the wide coverage characteristic of RFI, a U-former network with global information capture capabilities is utilized to learn the content covered by the interference and fill in the blanks created by the segmentation network. Compared to the traditional Transformer, this paper enhances its global information capture capabilities through shift-windows and down-sampling layers. Finally, the segmentation and inpainting networks are fused together through a weighted parameter for joint training. This not only accelerates the learning speed but also enables better coordination between the two networks, leading to improved RFI suppression performance. Extensive experimental results demonstrate the substantial performance enhancement of the proposed FuSINet. Compared to the PISNet+, the proposed attention mechanism achieves a 2.49 dB improvement in peak signal-to-noise ratio (PSNR). Furthermore, compared to Uformer, the FuSINet achieves an additional 4.16 dB improvement in PSNR.

MAGNETIC RESONANCE IMAGE DENOIZING USING A DUAL-CHANNEL DISCRIMINATIVE DENOIZING NETWORK

Magnetic Resonance Imaging (MRI) provides detailed information about soft tissues, which is essential for disease analysis. However, the presence of Rician noise in MR images introduces uncertainties that challenge medical practitioners during analysis. The objective of our research paper is to introduce an innovative dual-channel deep learning (DL) model designed to effectively denoize MR images. The methodology of this model integrates two distinct pathways, each equipped with unique normalization and activation techniques, facilitating the creation of a wide range of image features. Specifically, we employ Group Normalization in combination with Parametric Rectified Linear Units (PRELU) and Local Response Normalizations (LRN) alongside Scaled Exponential Linear Units (SELU) within both channels of our denoizing network. The outcomes of our proposed network exhibit clinical relevance, empowering medical professionals to conduct more efficient disease analysis. When evaluated by experienced radiologists, our results were deemed satisfactory. The network achieved a noteworthy improvement in performance metrics without requiring retraining. Specifically, there was a (6.65 ± 0.03)% enhancement in Peak Signal-to-Noise Ratio (PSNR) values and a (3.9 ± 0.02)% improvement in Structural Similarity Index (SSIM) values. Furthermore, when evaluated on the dataset on which the network was initially trained, the increase in PSNR and SSIM values was even more pronounced, with a (7.5 ± 0.02)% improvement in PSNR and a (4.3 ± 0.01)% enhancement in SSIM. Evaluation metrics, such as SSIM and PSNR, demonstrated a notable enhancement in the results obtained using our network. The statistical significance of our findings is evident, with p-values consistently less than 0.05 (p < 0.05). Importantly, our network demonstrates exceptional generalizability, as it performs remarkably well on different datasets without the need for retraining.

Two-and-a-half order score-based model for solving 3D ill-posed inverse problems

Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are crucial technologies in the field of medical imaging. Score-based models demonstrated effectiveness in addressing different inverse problems encountered in the field of CT and MRI, such as sparse-view CT and fast MRI reconstruction. However, these models face challenges in achieving accurate three dimensional (3D) volumetric reconstruction. The existing score-based models predominantly concentrate on reconstructing two-dimensional (2D) data distributions, resulting in inconsistencies between adjacent slices in the reconstructed 3D volumetric images. To overcome this limitation, we propose a novel two-and-a-half order score-based model (TOSM). During the training phase, our TOSM learns data distributions in 2D space, simplifying the training process compared to working directly on 3D volumes. However, during the reconstruction phase, the TOSM utilizes complementary scores along three directions (sagittal, coronal, and transaxial) to achieve a more precise reconstruction. The development of TOSM is built on robust theoretical principles, ensuring its reliability and efficacy. Through extensive experimentation on large-scale sparse-view CT and fast MRI datasets, our method achieved state-of-the-art (SOTA) results in solving 3D ill-posed inverse problems, averaging a 1.56 dB peak signal-to-noise ratio (PSNR) improvement over existing sparse-view CT reconstruction methods across 29 views and 0.87 dB PSNR improvement over existing fast MRI reconstruction methods with × 2 acceleration. In summary, TOSM significantly addresses the issue of inconsistency in 3D ill-posed problems by modeling the distribution of 3D data rather than 2D distribution which has achieved remarkable results in both CT and MRI reconstruction tasks.

An Adversarial Dual-Branch Network for Nonhomogeneous Dehazing in Tunnel Construction.

The tunnel construction area poses significant challenges for the use of vision technology due to the presence of nonhomogeneous haze fields and low-contrast targets. However, existing dehazing algorithms display weak generalization, leading to dehazing failures, incomplete dehazing, or color distortion in this scenario. Therefore, an adversarial dual-branch convolutional neural network (ADN) is proposed in this paper to deal with the above challenges. The ADN utilizes two branches of the knowledge transfer sub-network and the multi-scale dense residual sub-network to process the hazy image and then aggregate the channels. This input is then passed through a discriminator to judge true and false, motivating the network to improve performance. Additionally, a tunnel haze field simulation dataset (Tunnel-HAZE) is established based on the characteristics of nonhomogeneous dust distribution and artificial light sources in the tunnel. Comparative experiments with existing advanced dehazing algorithms indicate an improvement in both PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity) by 4.07 dB and 0.032 dB, respectively. Furthermore, a binocular measurement experiment conducted in a simulated tunnel environment demonstrated a reduction in the relative error of measurement results by 50.5% when compared to the haze image. The results demonstrate the effectiveness and application potential of the proposed method in tunnel construction.

Spatial and Channel Aggregation Network for Lightweight Image Super-Resolution.

Advanced deep learning-based Single Image Super-Resolution (SISR) techniques are designed to restore high-frequency image details and enhance imaging resolution through the use of rapid and lightweight network architectures. Existing SISR methodologies face the challenge of striking a balance between performance and computational costs, which hinders the practical application of SISR methods. In response to this challenge, the present study introduces a lightweight network known as the Spatial and Channel Aggregation Network (SCAN), designed to excel in image super-resolution (SR) tasks. SCAN is the first SISR method to employ large-kernel convolutions combined with feature reduction operations. This design enables the network to focus more on challenging intermediate-level information extraction, leading to improved performance and efficiency of the network. Additionally, an innovative 9 × 9 large kernel convolution was introduced to further expand the receptive field. The proposed SCAN method outperforms state-of-the-art lightweight SISR methods on benchmark datasets with a 0.13 dB improvement in peak signal-to-noise ratio (PSNR) and a 0.0013 increase in structural similarity (SSIM). Moreover, on remote sensing datasets, SCAN achieves a 0.4 dB improvement in PSNR and a 0.0033 increase in SSIM.

Parallel Photoelectron Storage and Visual Preprocessing Based on Nanowire Defect Engineering for Image Degradation

AbstractThe image degradation owing to the absorption and scattering of optical propagation medium makes the non‐visualization and anamorphose in real image. The complex algorithms based on highly integrated hardware can restore degraded image while it causes inefficient serial operation and storage redundancy. One improved strategy is to parallel functional integration in front‐end visual perceptron. Here, a parallel photoelectron storage and visual preprocessing in nanowire perceptron to synchronously achieve image perception, visual memory, and in‐sensor preprocessing are demonstrated. This functional integration is originated from charge‐resolved storage in temporal or frequency domain under optical pulsed excitation due to cascaded defect engineering. The proposed system enhances the peak signal‐to‐noise ratio (PSNR) of degraded image from 152 to 181 dB by feature decompression and also has 7.2% of PSNR improvement by noise filtration. This system is validated to improved train accuracy of back‐end artificial neural network (ANN) by 32.8% and shorten its iteration period by 77.4%.

The use of weather nowcasting convolutional neural network extrapolators in cardiac PET imaging.

Algorithms to predict short-term changes in local weather modalities have been used in meteorology for many years. These algorithms predict the temporospatial change in the movement of weather patterns such as cloud cover or precipitation. This paper extends convolutional neural network models for weather prediction/nowcasting to predict evolution in the extrapolation of sequentially acquired count data seen with cardiac positron tomography (PET) data to expected value temporally rather than spatially. Six different algorithms used for nowcasting were modified and applied to confirm the approach. These algorithms were trained on an image data set of both simulated ellipsoids and simulated cardiac PET data. The peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) were calculated for each of these trained models. They were compared to the BM3D denoising algorithm as a baseline comparison to a standard method of image denoising. Most of the implemented algorithms showed a significant improvement in both PSNR and SSIM when compared with the baseline standard, especially when the algorithms were implemented in combination. The best results were obtained with a combination of the ConvLSTM and TrajGRU algorithms with a PSNR improvement over the standard of 5 and more than double the SSIM metric. This approach of using serially acquired count data to extrapolate a future expected representation through convolutional neural networks has been shown to produce accurate representations of the expected value when compared with a baseline analytic methodology. This paper confirms that algorithms such as these can be used to substantially improve image estimation and shows significant improvement over a baseline standard.

Learned digital lens enabled single optics achromatic imaging.

High-quality imaging with reduced optical complexity has been extensively investigated owing to its promising future in academic and industrial research. However, the practical performance of most imaging systems has encountered a bottleneck posed by optics rather than electronics. Here, we propose a digital lens (DL) to compensate for the chromatic aberration induced by physical optical elements, while the residual wavelength-independent degradation is tackled through a self-designed neural network. By transforming physical aberration correction to an algorithm-based computational imaging task, the proposed DL enables our framework to reduce optical complexity and achieve achromatic imaging in the analog domain. Real experiments have been conducted with an off-the-shelf single lens and recovered images show up to 14.62 dB higher peak signal-to-noise ratio (PSNR) than the original chromatic input. Furthermore, we run a comprehensive ablation study to highlight the contribution of embedding the proposed DL, which shows a 4.83 dB PSNR improvement compared with the methods without DL. Technically, the proposed method can be an alternative for future applications that require both simple optics and high-fidelity visualization.

An Improved Medical Image Compression Method Based on Wavelet Difference Reduction

Advanced microscopic techniques such as high-throughput, high-content, multispectral, and 3D imaging could include many images per experiment requiring hundreds of gigabytes (GBs) of memory. Efficient lossy image-compression methods such as joint photographic experts group (JPEG) and JPEG 2000 are crucial to managing these large amounts of data. However, these methods can get visual quality with high compression ratios but do not necessarily maintain the medical data and information integrity. This paper proposes a novel and improved medical image compression method based on color wavelet difference reduction. Specifically, the proposed method is an extension of the standard wavelet difference reduction (WDR) method using mean co-located pixel difference to select the optimum quantity of color images that present the highest similarity in the spatial and temporal domain. The images with large spatiotemporal coherence are encoded as one volume and evaluated regarding the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). The proposed method is evaluated in the challenging histopathological microscopy image analysis field using 31 slides of colorectal cancer. It is found that the perceptual quality of the medical image is remarkably high. The results indicate that the PSNR improvement over existing schemes may reach up to 22.65 dB compared to JPEG 2000. Also, it can reach up to 10.33dB compared to a method utilizing discrete wavelet transform (DWT), leading us to implement a mobile and web platform that can be used for compressing and transmitting microscopic medical images in real time.

Real-time, single breath-hold, multi-slice, 2D cine radial MR image reconstruction using sc-GROG k-t ESPIRiT

Background: Multi-slice, multiple breath-hold ECG-gated 2D cine MRI is a standard technique for evaluating heart function and restricted to one or two images per breath-hold. Therefore, the standard cine MRI requires long scan time and can result in slice-misalignments because of various breath-hold locations in the multiple acquisitions. Methods: This work proposes the sc-GROG based k-t ESPIRiT with Total Variation (TV) constraint (sc-GROG k-t ESPIRiT) to reconstruct unaliased cardiac real-time cine MR images from highly accelerated whole heart multi-slice, single breath-hold, real-time 2D cine radial data acquired using the balanced steady-state free precession (trueFISP) sequence in 8 patients. The proposed method quality is assessed via Artifact Power (AP), Root-Mean Square Error (RMSE), Structure Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR), blood-pool to myocardial Contrast-to-Noise-Ratio (CNR), Signal-to-Noise-Ratio (SNR) and spatial-temporal intensity plots through the blood-myocardium boundary. The proposed method quantitative results are compared with the NUFFT based k-t ESPIRiT with Total Variation (TV) constraint (NUFFT k-t ESPIRiT) approach. Furthermore, clinical analysis and function quantification are assessed by Bland–Altman (BA) analyses. Results: As supported by the visual assessment and evaluation parameters, the reconstruction results of the sc-GROG k-t ESPIRiT approach provide an average 21%, 12%, 1% and 47% improvement in AP, RMSE, SSIM and PSNR, respectively in comparison to the NUFFT k-t ESPIRiT approach. Furthermore, the proposed method gives on average 45% and 58% improved blood-pool to myocardial CNR and SNR than the NUFFT k-t ESPIRiT approach. Also, from the BA plot, the proposed method gives better left ventricular and right ventricular function measurements as compared to the NUFFT k-t ESPIRiT scheme. Conclusions: The sc-GROG k-t ESPIRiT (Proposed Method) improves the spatio-temporal quality of the whole heart multi-slice, single breath-hold, real-time 2D cine radial MR and semi-automated analysis using standard clinical software, as compared to the NUFFT k-t ESPIRiT approach.

A densely interconnected network for deep learning accelerated MRI

ObjectiveTo improve accelerated MRI reconstruction through a densely connected cascading deep learning reconstruction framework.Materials and methodsA cascading deep learning reconstruction framework (reference model) was modified by applying three architectural modifications: input-level dense connections between cascade inputs and outputs, an improved deep learning sub-network, and long-range skip-connections between subsequent deep learning networks. An ablation study was performed, where five model configurations were trained on the NYU fastMRI neuro dataset with an end-to-end scheme conjunct on four- and eightfold acceleration. The trained models were evaluated by comparing their respective structural similarity index measure (SSIM), normalized mean square error (NMSE), and peak signal to noise ratio (PSNR).ResultsThe proposed densely interconnected residual cascading network (DIRCN), utilizing all three suggested modifications achieved a SSIM improvement of 8% and 11%, a NMSE improvement of 14% and 23%, and a PSNR improvement of 2% and 3% for four- and eightfold acceleration, respectively. In an ablation study, the individual architectural modifications all contributed to this improvement for both acceleration factors, by improving the SSIM, NMSE, and PSNR with approximately 2–4%, 4–9%, and 0.5–1%, respectively.ConclusionThe proposed architectural modifications allow for simple adjustments on an already existing cascading framework to further improve the resulting reconstructions.

An Efficient Parallel Block Compressive Sensing Scheme for Medical Signals and Image Compression

Compressive Sensing or Compressed sensing (CS) is a latest technique used for compression of medical signals and medical images which benefits both the speed and accuracy. The performance of CS based compression is mostly dependent on decoding methods rather than the CS encoding methods used in practice. It has been found in literature that CS encoding algorithms have got least importance than decoding algorithms. In this paper an efficient CS encoding scheme based on modified parallel block processing has been suggested for biomedical signal and image compression. The input signals and images are acquired and preprocessed with suitable filtering techniques and then the same have been divided into number of cells and blocks. Each block is then processed in parallel to enable faster computation. Three performance indices, i.e., the peak signal to noise ratio (PSNR), reconstruction time (RT) and structural similarity index (SSIM) have been analyzed with respect to the compression ratio. A comparative study has been carried out between the standard CS based compression and the suggested technique. The results showed that proposed algorithm provides better performance than standard CS based compression. More specifically, the parallel block CS reported the best results than standard CS with respect to less reconstruction time and satisfactory PSNR and SSIM. The suggested technique offers SSIM improvement approximately by 8% and reduction in RT by 99% than the standard CS based compression for CT scan image compression. In case of brain signal compression, the suggested technique offers SSIM improvement approximately by 25%, PSNR improvement by around 2% and reduction in RT by 75% than the standard CS.

A Novel Type-2 Fuzzy Directed Hybrid Post-Filtering Technique for Efficient JPEG Image Artifact Reduction

Image and video compression becomes very popular and intense field in recent decades, due to their fast and quality communication demand. To cope up with the high speed communication demand of image and videos, over the communication channel, higher compression (ie. low bit rate compression) is now preferred at the cost of quality degradation. Currently JPEG and JPEG2000 are the most popularly used codec’s for achieving quality image compression. Practically, on the low bit rate compression, it has been observed that, the JPEG standard compressed images suffer from multifarious visual distortions. To address this problem effectively, this paper proposed an innovative type-2 fuzzy directed hybrid post filtering technique, which is framed to suppress the artifacts generated in JPEG compressed images at low bit rate compression. The proposed technique addresses all three types of JPEG compressed image artifacts: blocking, edges blurring, and aliasing. Furthermore the proposed technique is structured with two stages, to enhance the quality of JPEG compressed images. The first stage, removes blocking artifacts using boundary smoothing and guided filtering techniques. The second stage reduces blurring and aliasing around the edges through type-2 fuzzy directed local edge regeneration. To prove higher efficiency of proposed work, a complete comparative performance evaluation with other existing JPEG artifact removal techniques, has also presented. This extensive performance analysis includes visual quality assessment of post-filtered images along with subjective quality assessment on the basis of, Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE). Performance evaluation illustrates that the, proposed approach is efficient, provides PSNR improvement of approximately 1 dB along with higher reduction in MSE values as compared to state of the art algorithms for all the bit-rate compressions.

Denoising wrist pulse signals using variance thresholding technique

Background/Objectives: Denoising of the wrist pulse is a significant preprocessing stage for accurate investigation of the disease. The objective is to improve and analyze performance metrics of denoising techniques. Methods/Statistical analysis: Denoising of wrist pulse with the evaluation parameters such as PSNR, SNR, AE and RMSE has been implemented using wavelets such as Daubechies, Symlet and Biorthogonal. The performance of wavelets depends on the choice of decomposition level N and thresholding techniques. Findings: Variance thresholding technique showed significant improvement in Peak Signal to Noise Ratio (PSNR), Signal to Noise Ratio (SNR) and reduction in Absolute Error (AE) and Root Mean Square Error (RMSE) compared to other thresholding methods. Novelty/Applications: Experimental results showed drastic improvement in PSNR and SNR retaining the pathophysiological information of the wrist pulse signal for future analysis.

Real-Time Light Field Denoising Using a Novel Linear 4-D Hyperfan Filter

Four-dimensional (4-D) light fields (LFs) enable novel imaging technologies, which are traditionally based on two-dimensional images. In most of these applications, denoising of LFs is required as a preprocessing technique before any subsequent processing. We propose a real-time LF denoising method using a novel 4-D linear and shift-invariant hyperfan filter. The proposed method exploits sparsity of the spectrum of a LF and the 4-D hyperfan filter is implemented in the 4-D mixed-domain (i.e.,two-dimensional space and two-dimensional frequency) leading to significant reductions in computational and memory complexities. A software implementation of the proposed method provides better or comparable denoising performance for grayscale and color LFs with respect to the metrics peak-signal-to-noise ratio (PSNR) and structural similarity (SSIM) compared to previously reported linear LF denoising methods, while reducing the processing time approximately by 66% and 31% for grayscale and color LFs, respectively. Furthermore, we propose a semi-systolic hardware architecture for the proposed denoising method, and implement on a field-programmable gate array (FPGA). The FPGA implementation implies a throughput of 25 LFs/s for LFs of size 11×11×625×434 and provides approximately 13 dB improvement in PSNR and 0.7 improvement in SSIM for grayscale LFs verifying the suitability for real-time processing.

An Efficient Image Denoising Method based on Bilateral filter Model and Neighshrink SURE

In all the instances of image acquisition, transmission and storage, the unwanted noise gets into the information content of the image and thereby introduces an unpleasant visual quality to the observer. So the field of image processing has produced a lot of image denoising algorithms and techniques to improve the visual quality of the image. Since noise cannot be reduced to zero practically, the need for faithful and efficient denoising techniques to produce almost noiseless images demands a systematic research work in the field of denoising methods. The denoising process using a bilateral filter even though produces improvement in the image quality, it does not show consistency when the noise level is high and also the peak signal to noise ratio (PSNR) and Image quality Index (IQI) do not show any improvement. This paper proposes an improved algorithm that incorporates the function of bilateral filter model and wavelet thresholding using Neighshrink SURE method. The results show significant improvement in both PSNR and IQI values with respect to the four standard test images under various noise conditions.

An approach for de-noising and contrast enhancement of retinal fundus image using CLAHE

Now-a-days medical fundus images are widely used in clinical diagnosis for the detection of retinal disorders. Fundus images are generally degraded by noise and suffer from low contrast issues. These issues make it difficult for ophthalmologist to detect and interpret diseases in fundus images. This paper presents a noise removal and contrast enhancement algorithm for fundus image. Integration of filters and contrast limited adaptive histogram equalization (CLAHE) technique is applied for solving the issues of de-noising and enhancement of color fundus image. The efficacy of the proposed method is evaluated through different performance parameters like Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), Correlation coefficient (CoC) and Edge preservation index (EPI). The proposed method achieved 7.85% improvement in PSNR, 1.19% improvement in SSIM, 0.12% improvement in CoC and 1.28% improvement in EPI when compared to the state of the art method.

1.5-D Sparse Array for Millimeter-Wave Imaging Based on Compressive Sensing Techniques

The goal of this paper is to reduce the antenna count in a millimeter (mm)-wave imaging system by proposing both hardware and software solutions. The concept of image sparsity in the transform domain is utilized to present the compressive sensing (CS) formulation for both mono-static and multistatic imaging at mm-wave frequencies. To reduce the complexity of the imaging system and reconstruction process, we introduce 1.5-D array structure, which is a random sparse array with orthogonal element locations. It is shown that the peak signal-to-noise ratio (PSNR) of the reconstructed image obtained by a 1.5-D array with 65% sparsity is very close to the PSNR of a uniform 2-D array for mono-static imaging. Moreover, for multistatic imaging, it is illustrated that a high-quality image is reconstructed with 92% reduction in antenna elements, which significantly reduces the cost, scan time, and system complexity. Furthermore, a 1.5-D array of 16 patch antennas with 60% sparsity, operating from 29 to 32 GHz is designed and implemented to investigate the performance of the proposed 1.5-D array for imaging, as well as the presented CS-based reconstruction method. Measurements show that the quality of the reconstructed image using this 1.5-D array with CS-based reconstruction is very close to a full 2-D array; for example, the PSNR achieved for the reconstructed image of a knife is 33 dB referenced to a uniform 2-D array. The proposed reconstruction method results in 6–11 dB improvement in PSNR compared with the conventional reconstruction methods with a reasonable increase in computing load.