Quality Of Reconstructed Images Research Articles

Eye-Inspired Single-Pixel Imaging with Lateral Inhibition and Variable Resolution for Special Unmanned Vehicle Applications in Tunnel Inspection

This study presents a cutting-edge imaging technique for special unmanned vehicles (UAVs) designed to enhance tunnel inspection capabilities. This technique integrates ghost imaging inspired by the human visual system with lateral inhibition and variable resolution to improve environmental perception in challenging conditions, such as poor lighting and dust. By emulating the high-resolution foveal vision of the human eye, this method significantly enhances the efficiency and quality of image reconstruction for fine targets within the region of interest (ROI). This method utilizes non-uniform speckle patterns coupled with lateral inhibition to augment optical nonlinearity, leading to superior image quality and contrast. Lateral inhibition effectively suppresses background noise, thereby improving the imaging efficiency and substantially increasing the signal-to-noise ratio (SNR) in noisy environments. Extensive indoor experiments and field tests in actual tunnel settings validated the performance of this method. Variable-resolution sampling reduced the number of samples required by 50%, enhancing the reconstruction efficiency without compromising image quality. Field tests demonstrated the system’s ability to successfully image fine targets, such as cables, under dim and dusty conditions, achieving SNRs from 13.5 dB at 10% sampling to 27.7 dB at full sampling. The results underscore the potential of this technique for enhancing environmental perception in special unmanned vehicles, especially in GPS-denied environments with poor lighting and dust.

An optimisation method for coplanar array capacitor image reconstruction based on Chambolle-Pock framework with automatic parameter selection

ABSTRACT Coplanar array capacitance imaging is an attractive imaging technique in industrial and detection fields, but low-quality images reduce its practicality and reliability. The inverse problem is difficult to solve and the image reconstruction quality is low, in this paper, an auxiliary optimisation strategy is proposed to transform the inverse problem of image reconstruction into a general minimisation problem, combined with the threshold of automatic parameter selection for feature extraction of capacitive data. The Kullback-Leibler(KL) data error term is combined with the Total Variation (TV) semi-norm to form a new imaging model. Utilising the Chambolle-Pock framework, the image reconstruction problem is transformed into a general minimisation problem to obtain high-quality images. Capacitance data from a 3 × 4 coplanar array capacitance sensor is used for feature extraction, serving as the basis for automatic parameter selection in the auxiliary optimisation strategy. The proposed Chambolle-Pock framework with the support of the auxiliary optimisation strategy, validated through simulation and experiments, outperforms popular imaging algorithms in terms of performance. The novel automatic parameter selection optimization algorithm introduced in this paper enhances the accuracy of capacitor image reconstruction in coplanar arrays.

Comparative study of generalized-sampling-theorem-based digital super-resolution for 2D data

The size of pixels in a digital recording device, such as a CCD array, limits the spatial resolution in images obtained by an optical imaging system, thereby degrading the image quality. Digital super-resolution (DSR) techniques are used to reconstruct a high-resolution (HR) image from multiple sub-pixel-shifted low-resolution images in order to improve the image quality. In this article, we formulate a mathematical framework for DSR using the generalized sampling theorem (GST). The GST-based DSR method’s performance is evaluated by comparing it to existing resolution enhancement methods on the basis of evaluation metrics like percentage mean square error (%MSE), structural similarity index measure (SSIM), and peak signal-to-noise ratio (PSNR). The GST DSR method exhibits an overall superior quality image reconstruction based on quantitative analysis (with a near zero %MSE, SSIM around one, and improved PSNR in dB) and qualitative comparison. The robustness of the GST DSR method is further demonstrated in the presence of frame-to-frame shift estimation error using %MSE and SSIM by comparing it with multi-frame interpolation approaches.

Angiographic reconstructions of CT perfusion for occlusion detection in ischemic stroke

BackgroundBoth CT angiography and CT perfusion involve the administration and tracking of a contrast medium bolus for different purposes. In this study, we aim to compare the diagnostic accuracy and subjective image quality of CTP-angiographic reconstructions with conventional CTA for occlusion detection in ischemic stroke patients. MethodsIn this retrospective study, patients with a final diagnosis of ischemic stroke and who underwent both CTA and CTP from September 2020 up to and including September 2021 were included. CTP-AR was reconstructed from the 1 mm CTP series at the time of maximum arterial inflow. Three readers with different levels of experience assessed both CTA and CTP for occlusion detection, expressed certainty, and rated subjective image quality. The reference standard was set at the consensus meeting. Pooled sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. Differences in subjective and objective image quality and certainty were assessed using Kendall’s tau correlation and paired samples t-tests. ResultsIn total, 107/210 included patients had an occlusion based on our reference standard. Pooled sensitivity and specificity for occlusion detection were 90 % (95 %CI 85–93 %) and 94 % (95 %CI 90–97 %) for CTA, and 89 % (95 %CI 84–93 %) and 93 % (95 %CI 89–96) for CTP-AR, respectively. The pooled certainty did not significantly differ between CTA and CTP-AR (P=.43). The pooled subjective image quality scores significantly differed between CTA and CTP-AR (τ = 0.3, P < 0.001), where one reader rated subjective image quality higher in CTA and two readers in CTP-AR. ConclusionCTP-AR has comparable diagnostic accuracy to conventional CTA imaging for occlusion detection in ischemic stroke patients. Thus, using CTP-AR instead of CTA plus CTP may conserve contrast medium.

Revealing the Dark Side of Non-Local Attention in Single Image Super-Resolution.

Single Image Super-Resolution (SISR) aims to reconstruct a high-resolution image from its corresponding low-resolution input. A common technique to enhance the reconstruction quality is Non-Local Attention (NLA), which leverages self-similar texture patterns in images. However, we have made a novel finding that challenges the prevailing wisdom. Our research reveals that NLA can be detrimental to SISR and even produce severely distorted textures. For example, when dealing with severely degrade textures, NLA may generate unrealistic results due to the inconsistency of non-local texture patterns. This problem is overlooked by existing works, which only measure the average reconstruction quality of the whole image, without considering the potential risks of using NLA. To address this issue, we propose a new perspective for evaluating the reconstruction quality of NLA, by focusing on the sub-pixel level that matches the pixel-wise fusion manner of NLA. From this perspective, we provide the approximate reconstruction performance upper bound of NLA, which guides us to design a concise yet effective Texture-Fidelity Strategy (TFS) to mitigate the degradation caused by NLA. Moreover, the proposed TFS can be conveniently integrated into existing NLA-based SISR models as a general building block. Based on the TFS, we develop a Deep Texture-Fidelity Network (DTFN), which achieves state-of-the-art performance for SISR. Our code and a pre-trained DTFN are available on GitHub† for verification.

A Novel RPCA Method Using Log-Weighted Nuclear and L_(2,1) Norms Combined with Contrast-Limited Adaptive Histogram Equalization (CLAHE) for High Dimensional Natural and Medical Image Data

Estimating the true underlying images from distorted high-dimensional data is crucial for applications in high-profile fields such as crime detection in security, clinical settings and medical diagnosis in healthcare, and radar imaging in signal processing. Existing statistical methods often struggle with robustness and image reconstruction quality when processing high-dimensional image data. While Robust Principal Component Analysis (RPCA) is widely used for image recovery, its reliance on uniform weights with singular value decomposition (SVD) weakens performance, especially in noisy environments. The L1 norm also fails to capture image details and recovery under high noise levels, a critical limitation for applications like medical diagnoses, where detail is essential. These challenges emphasize the need for improved methods to handle noise and enhance image quality in sensitive fields. Therefore, this paper proposes a novel RPCA method that integrates CLAHE with Log weighted nuclear norm (LWNN) and the L2,1 norm for high-dimensional natural and medical imaging. To reduce the computational load, our novel method is formulated into a new optimization problem and solved using the Alternating Direction Method of Multipliers (ADMM). This method leverages LWNN for enhanced low-rank approximation to drastically prune out the anomalies in images and the norm for improved sparse component recovery. Our approach has superior performance in image reconstruction compared to other state-of-the-art methods (SOTAs), showing significant advancements with real-world datasets. An interesting finding of this research is that combining the LWNN with the L2,1 norm is highly effective at removing noise from images. Furthermore, when the CLAHE technique is combined with LWNN and the L2,1 norm, it significantly enhances the extraction of previously unseen features, making blood vessels in medical images much clearer and more distinguishable. This combination proves to be a powerful approach for medical image analysis, revealing details that are otherwise difficult to detect. This method will be used for crime detection in security intelligence, and clinical settings and medical diagnosis in human retinal eyes.

Influence and analysis of measurement errors of the complex coherence’s modulus on reconstructed image quality

Influence and analysis of measurement errors of the complex coherence’s modulus on reconstructed image quality

Deep Learning Reconstruction for Enhanced Resolution and Image Quality in Breath-Hold MRCP: A Preliminary Study

Objective This preliminary study aims to assess the image quality of enhanced-resolution deep learning reconstruction (ER-DLR) in magnetic resonance cholangiopancreatography (MRCP) and compare it with non-ER-DLR MRCP images. Methods Our retrospective study incorporated 34 patients diagnosed with biliary and pancreatic disorders. We obtained MRCP images using a single breath-hold MRCP on a 3T MRI system. We reconstructed MRCP images with ER-DLR (matrix = 768 × 960) and without ER-DLR (matrix = 256 × 320). Quantitative evaluation involved measuring the signal-to-noise ratio (SNR), contrast, contrast-to-noise ratio (CNR) between the common bile duct and periductal tissues, and slope. Two radiologists independently scored image noise, contrast, artifacts, sharpness, and overall image quality for the 2 image types using a 4-point scale. Results are expressed as median and interquartile range (IQR), and we compared quantitative and qualitative scores employing the Wilcoxon test. Results In quantitative analyses, ER-DLR significantly improved SNR (21.08 [IQR: 14.85, 31.5] vs 15.07 [IQR: 9.57, 25.23], P &lt; 0.001), CNR (19.29 [IQR: 13.87, 24.98] vs 11.23 [IQR: 8.98, 15.74], P &lt; 0.001), contrast (0.96 [IQR: 0.94, 0.97] vs 0.9 [IQR: 0.87, 0.92], P &lt; 0.001), and slope of MRCP (0.62 [IQR: 0.56, 0.66] vs 0.49 [IQR: 0.45, 0.53], P &lt; 0.001). The qualitative evaluation demonstrated significant improvements in the perceived noise (P &lt; 0.001), contrast (P = 0.013), sharpness (P &lt; 0.001), and overall image quality (P &lt; 0.001). Conclusions ER-DLR markedly increased the resolution, SNR, and CNR of breath-hold-MRCP compared to cases without ER-DLR.

Deep learning enables the use of ultra-high-density array in DNBSEQ

DNBSEQ employs a patterned array to facilitate massively parallel sequencing of DNA nanoballs (DNBs), leading to a considerable boost in throughput. By employing the ultra-high-density (UHD) array with an increased density of DNB binding sites, the throughput of DNBSEQ can be further expanded. However, the typical imaging system of the DNBSEQ sequencer is unable to resolve adjacent DNBs spaced smaller than the resolution limit, resulting in poor base-calling performance of the UHD array and hindering its practical application. In this study, we propose a deep-learning-based DNB image super-resolution network named DNBSRN to address this problem. DNBSRN has a specifically designed structure for DNB images and employs a histogram-matching-based preprocessing approach. For the eight DNB image datasets generated from the DNBSEQ sequencer using UHD arrays with 360 nm pitch, the base-calling performances are significantly improved after super-resolution reconstruction by DNBSRN and reached a comparable level to those of the regular density array. In terms of reconstruction speed, DNBSRN takes only 7.61 ms for an input image with 500 × 500 pixels, which minimizes its influence on throughput. Furthermore, compared with state-of-the-art super-resolution networks, DNBSRN demonstrates superior performance in terms of both the quality and speed of DNB image reconstruction. DNBSRN successfully addresses the DNB image super-resolution task. Integrating DNBSRN into the image analysis workflow of DNBSEQ will allow for the application of UHD array, hence enabling a considerable improvement in throughput as well as tremendous savings in unit reagent cost.

Application of Bivariate Reproducing Kernel-Based Best Interpolation Method in Electrical Tomography.

Electrical Tomography (ET) technology is widely used in multiphase flow detection due to its advantages of low cost, visualization, fast response, non-radiation, and non-invasiveness. However, ill-posed solutions lead to low image reconstruction resolution, which limits its practical engineering applications. Although existing interpolation approximation algorithms can alleviate the effects of the ill-posed solutions to some extent, the imaging results remain suboptimal due to the limited approximation capability of these methods. This paper proposes a Bivariate Reproducing Kernel-Based Best Interpolation (BRKBI) method, which offers smaller approximation errors and clearer image reconstruction quality compared to existing methods. The effectiveness of the BRKBI method is validated through theoretical analysis and experimental comparisons.

Counterfactual Diffusion Models for Mechanistic Explainability of Artificial Intelligence Models in Pathology.

Deep learning can extract predictive and prognostic biomarkers from histopathology whole slide images, but its interpretability remains elusive. We develop and validate MoPaDi (Morphing histoPathology Diffusion), which generates counterfactual mechanistic explanations. MoPaDi uses diffusion autoencoders to manipulate pathology image patches and flip their biomarker status by changing the morphology. Importantly, MoPaDi includes multiple instance learning for weakly supervised problems. We validate our method on four datasets classifying tissue types, cancer types within different organs, center of slide origin, and a biomarker - microsatellite instability. Counterfactual transitions were evaluated through pathologists' user studies and quantitative cell analysis. MoPaDi achieves excellent image reconstruction quality (multiscale structural similarity index measure 0.966-0.992) and good classification performance (AUCs 0.76-0.98). In a blinded user study for tissue-type counterfactuals, counterfactual images were realistic (63.3-73.3% of original images identified correctly). For other tasks, pathologists identified meaningful morphological features from counterfactual images. Overall, MoPaDi generates realistic counterfactual explanations that reveal key morphological features driving deep learning model predictions in histopathology, improving interpretability.

Spatial and Modal Optimal Transport for Fast Cross-Modal MRI Reconstruction.

Multi-modal magnetic resonance imaging (MRI) plays a crucial role in comprehensive disease diagnosis in clinical medicine. However, acquiring certain modalities, such as T2-weighted images (T2WIs), is time-consuming and prone to be with motion artifacts. It negatively impacts subsequent multi-modal image analysis. To address this issue, we propose an end-to-end deep learning framework that utilizes T1-weighted images (T1WIs) as auxiliary modalities to expedite T2WIs' acquisitions. While image pre-processing is capable of mitigating misalignment, improper parameter selection leads to adverse pre-processing effects, requiring iterative experimentation and adjustment. To overcome this shortage, we employ Optimal Transport (OT) to synthesize T2WIs by aligning T1WIs and performing cross-modal synthesis, effectively mitigating spatial misalignment effects. Furthermore, we adopt an alternating iteration framework between the reconstruction task and the cross-modal synthesis task to optimize the final results. Then, we prove that the reconstructed T2WIs and the synthetic T2WIs become closer on the T2 image manifold with iterations increasing, and further illustrate that the improved reconstruction result enhances the synthesis process, whereas the enhanced synthesis result improves the reconstruction process. Finally, experimental results from FastMRI and internal datasets confirm the effectiveness of our method, demonstrating significant improvements in image reconstruction quality even at low sampling rates.

A new method of reconstructing images of gamma-ray telescopes applied to the LST-1 of CTAO

Context. Imaging atmospheric Cherenkov telescopes (IACTs) are used to observe very high-energy photons from the ground. Gamma rays are indirectly detected through the Cherenkov light emitted by the air showers they induce. The new generation of experiments, in particular the Cherenkov Telescope Array Observatory (CTAO), sets ambitious goals for discoveries of new gamma-ray sources and precise measurements of the already discovered ones. To achieve these goals, both hardware and data analysis must employ cuttingedge techniques. This also applies to the LST-1, the first IACT built for the CTAO, which is currently taking data on the Canary island of La Palma. Aims. This paper introduces a new event reconstruction technique for IACT data, aiming to improve the image reconstruction quality and the discrimination between the signal and the background from misidentified hadrons and electrons. Methods. The technique models the development of the extensive air shower signal, recorded as a waveform per pixel, seen by CTAO telescopes’ cameras. Model parameters are subsequently passed to random forest regressors and classifiers to extract information on the primary particle. Results. The new reconstruction was applied to simulated data and to data from observations of the Crab Nebula performed by the LST-1. The event reconstruction method presented here shows promising performance improvements. The angular and energy resolution, and the sensitivity, are improved by 10 to 20% over most of the energy range. At low energy, improvements reach up to 22%, 47%, and 50%, respectively. A future extension of the method to stereoscopic analysis for telescope arrays will be the next important step.

A novel recursive sub-tensor hyperspectral compressive sensing of plant leaves based on multiple arbitrary-shape regions of interest

Plant hyperspectral images (HSIs) contain valuable information for agricultural disaster prediction, biomass estimation, and other applications. However, they also include a lot of irrelevant background information, which wastes storage resources. In this paper, we propose a novel recursive sub-tensor hyperspectral compressive sensing method for plant leaves. This method uses recursive sub-tensor compressive sensing to compress and reconstruct each arbitrary-shape leaf region, discarding a large amount of background information to achieve the best possible reconstruction performance of the leaf region and significantly reduce storage space. The proposed method involves several key steps. Firstly, the optimal band is determined using the spatial spectral decorrelation criterion, and its corresponding mask image is used to extract the leaf regions from the background. Secondly, the recursive maximum inscribed rectangle algorithm is applied to obtain rectangular sub-tensors of leaves recursively. Each sub-tensor is then individually compressed and reconstructed. Finally, all sub-tensors can be reconstructed to form complete leaf HSIs without background information. Experimental results demonstrate that the proposed method achieves superior image reconstruction quality at extremely low sampling rates compared to other methods. The proposed method can improve average Peak Signal-to-Noise Ratio (PSNR) values by about 3.04% and 0.74% compared to Tensor Compressive Sensing (TCS) at the sampling rate of 2%. In the spectral domain, the proposed method can achieve significantly smaller Spectral Angle Mapper (SAM) values and relatively lower spectral indices errors for Double Difference, Triangular Vegetation Index, Leaf Chlorophyll Index, and Modified Normalized Difference 680 than those of TCS. Therefore, the proposed method achieves better compression performance for reconstructed plant leaf HSIs than the other methods.

A companion matrix-based efficient image encryption method

The increased use of multimedia applications to share digital images has raised concerns about their security during transmission and storage as well. Thus, the need for integrating the chaotic system with compressive sensing became an important and potentially successful method for enhancing the image security. However, in the integration of a single One-dimensional (1-D) chaotic system with compressive sensing, there is a significant drawback that is the limited chaotic behaviour and key space, which makes it vulnerable against brute force and statistical attacks. Hence, enlarging the key space to improve security by using a single One-Dimensional (1-D) chaotic system and making it resilient against brute force attacks still needs to be addressed.In this paper, we propose an encryption method that makes use of the notion of a companion matrix and a single One-Dimensional (1-D) chaotic system to enlarge the key space. This method converts the grayscale image into a sparse representation. Thereafter, this sparse matrix is shuffled by applying the Arnold Cat Map, where the parameters for this map are generated through the usage of a One-Dimensional (1-D) Piecewise Linear Chaotic Map. Furthermore, we construct the key matrix by computing the eigenvalues of the companion matrix, and then we diffuse the cipher image to improve the security against statistical attacks.Experimental results demonstrate that the proposed method balances the security and image reconstruction quality effectively. The advantage of the proposed method is that even by using a single One-Dimensional (1-D) chaotic system (i.e., faster in implementation), by using the concept of companion matrix, it achieves a significantly larger key space of 2400 that is larger than the several existing state-of-the-art methods that use hyperchaotic systems.

A Knowledge Base Driven Task-Oriented Image Semantic Communication Scheme

With the development of artificial intelligence and computer hardware, semantic communication has been attracting great interest. As an emerging communication paradigm, semantic communication can reduce the requirement for channel bandwidth by extracting semantic information. This is an effective method that can be applied to image acquisition of unmanned aerial vehicles, which can transmit high-data-volume images within the constraints of limited available bandwidth. However, the existing semantic communication schemes fail to adequately incorporate the guidance of task requirements into the semantic communication process and are difficult to adapt to the dynamic changes of tasks. A task-oriented image semantic communication scheme driven by knowledge base is proposed, aiming at achieving high compression ratio and high quality image reconstruction, and effectively solving the bandwidth limitation. This scheme segments the input image into several semantic information unit under the guidance of task requirements by Yolo-World and Segment Anything Model. The assigned bandwidth for each unit is according to the task relevance scores, which enables high-quality transmission of task-related information with lower communication overheads. An improved metric weighted learned perceptual image patch similarity (LPIPS) is proposed to evaluate the transmission accuracy of the novel scheme. Experimental results show that our scheme achieves a notable performance improvement on weighted LPIPS while the same compression ratio compared with traditional image compression schemes. Our scheme has a higher target capture ratio than traditional image compression schemes under the task of target detection.

Reconstruction of reservoir rock using attention-based convolutional recurrent neural network

The digital reconstruction of reservoir rock or porous media is important as it enables us to visualize and explore their real internal structures. The reservoir rocks (such as sandstone and carbonate) contain both spatial and temporal characteristics, which pose a big challenge in their characterization through routine core analysis or x-ray microcomputer tomography. While x-ray micro-computed tomography gives us three-dimensional images of the porous media, it is often impossible to quantify the variability of the pore, grains, structure, and orientation experimentally. Recently, machine learning has successfully demonstrated the reconstruction ability of reservoir rock images or any porous media. These reservoir rock images are crucial for the digital characterization of the reservoir. We propose a novel algorithm consisting of the convolutional neural network, an attention mechanism, and a recurrent neural network for the reconstruction of reservoir rock or porous media images. The attention-based convolutional recurrent neural network (ACRNN) can reconstruct a representative sample of reservoir rocks. The reconstructed image quality was checked by comparing them with the original Parker sandstone, Leopard sandstone, carbonate shale, Berea sandstone, and sandy medium images. We evaluated the reconstruction by measuring pore and throat properties, two-point probability function, and structural similarity index. Results show that ACRNN can reconstruct reservoir rock or porous media of different scales with approximately the same geometrical, statistical, and topological parameters of the reservoir rock images. This deep learning method is computationally efficient, fast, and reliable for synthetic image realizations. The model was trained and validated on real images, and the reconstructed images showed excellent concordance with the real images having almost the same pore and grain structures. The deep learning-based digital rock reconstruction of reservoir rock or porous media images can aid in rapid image generation to better understand reservoir rock or subsurface formation.

Enhancing Ischemic Stroke Evaluation by a Model-Based Photoacoustic Tomography Algorithm.

Ischemic stroke (IS) is characterized by the sudden interruption of blood supply to the brain, resulting in neurological impairments and even mortality. Photoacoustic computed tomography (PACT) integrates the high contrast of optical imaging and the penetration of ultrasound imaging, enabling non-invasive IS evaluation. However, the image reconstruction quality significantly affects the oxyhemoglobin saturation (sO2) estimation. This study investigates a model-based with total variation minimized by augmented Lagrangian and alternating direction (MB-TVAL3) approach and compared it with the widely used back-projection (BP) and delay-and-sum (DAS) algorithms. Both simulations and invivo experiments are conducted to validate the performance of the MB-TVAL3 algorithm, showing a higher sO2 estimation accuracy and sensitivity in detecting infarct area compared to BP and DAS. The findings of this study emphasize the impact of acoustic inverse problem on the accuracy of sO2 estimation and the proposed approach offers valuable support for IS evaluation and cerebrovascular diagnosis.

Real-Time Nonlinear Image Reconstruction in Electrical Capacitance Tomography Using the Generative Adversarial Network

This study investigated the potential of the generative adversarial neural network (cGAN) image reconstruction in industrial electrical capacitance tomography. The image reconstruction quality was examined using image patterns typical for a two-phase flow. The training dataset was prepared by generating images of random test objects and simulating the corresponding capacitance measurements. Numerical simulations were performed using the ECTsim toolkit for MATLAB. A cylindrical sixteen-electrode ECT sensor was used in the experiments. Real measurements were obtained using the EVT4 data acquisition system. The reconstructed images were evaluated using selected image quality metrics. The results obtained using cGAN are better than those obtained using the Landweber iteration and simplified Levenberg–Marquardt algorithm. The suggested method offers a promising solution for a fast reconstruction algorithm suitable for real-time monitoring and the control of a two-phase flow using ECT.

Adaptive support constraint based on pixel difference distribution for twin-image removal in in-line holography

A tight support domain can enhance the reconstruction effect of phase retrieval algorithms using a support domain constraint. Hence, an adaptive support method based on pixel difference distribution to calculate a more accurate support domain is proposed in this paper. The support domain is continuously updated at each iteration of the phase retrieval algorithm, resulting in a support domain that closely corresponds to the shape and size of the object. Simulations are conducted to investigate the reconstruction effect of complex-valued objects as well as the influence of varying the binarization threshold and the difference window size on the convergence performance in the absence of noise. The changing dynamics of the support domain during the iterative process are also showcased. The experiments demonstrate that the adaptive support method can improve the reconstruction image quality and image contrast under actual conditions. Both simulations and experiments affirm the feasibility of this approach.