Distortion Correction Research Articles

Algorithm and software for field distortion correction in a commercial SD-OCT for corneal curvature assessment.

Accurate assessment of corneal curvatures using frequency domain optical coherence tomography (OCT) with galvanometer scanners remains challenging due to the well-known scan field distortion. This paper presents an algorithm and software for correcting the distortion using only two simple measurements in which a readily available standard sphere is positioned in different depths in front of the OCT scanner. This offers a highly accessible and easily reproducible method for the field distortion correction (FDC). The correction was validated by measuring different spherical phantoms and conducting corneal curvature measurements of ex vivo porcine corneas using a commercial spectral-domain OCT system and a clinically approved swept-source OCT as a reference instrument. Thus, the error in radius measurements of spherical phantoms was reduced by >90% and astigmatism by >80% using FDC. In explanted porcine eyes, the error in astigmatism measurements with the Telesto was reduced by 75% for power and 70% for angle. The best fitting sphere radius was determined up to a deviation of 0.4% from the Anterion. This paper describes a correction algorithm for OCT immanent distortion that is applicable to any scanning OCT setup and enables precise corneal curvature measurements. The MATLAB software for the FDC is publicly available on GitHub.

Distortion Correction and Denoising of Light Sheet Fluorescence Images.

Light Sheet Fluorescence Microscopy (LSFM) has emerged as a valuable tool for neurobiologists, enabling the rapid and high-quality volumetric imaging of mice brains. However, inherent artifacts and distortions introduced during the imaging process necessitate careful enhancement of LSFM images for optimal 3D reconstructions. This work aims to correct images slice by slice before reconstructing 3D volumes. Our approach involves a three-step process: firstly, the implementation of a deblurring algorithm using the work of K. Becker; secondly, an automatic contrast enhancement; and thirdly, the development of a convolutional denoising auto-encoder featuring skip connections to effectively address noise introduced by contrast enhancement, particularly excelling in handling mixed Poisson-Gaussian noise. Additionally, we tackle the challenge of axial distortion in LSFM by introducing an approach based on an auto-encoder trained on bead calibration images. The proposed pipeline demonstrates a complete solution, presenting promising results that surpass existing methods in denoising LSFM images. These advancements hold potential to significantly improve the interpretation of biological data.

A Method for Solving Ill-Conditioned Nonlinear Least Squares Problems and Its Application in Image Distortion Correction Using Self-Calibration

In this study, the ill-conditioning of the iterative method for nonlinear models is discussed. Due to the effectiveness of ridge estimation for ill-conditioned problems and the lack of a combination of the H-K formula with the iterative method, the improvement of the LM algorithm is studied in this paper. Considering the LM algorithm for ill-conditioned nonlinear least squares, an improved LM algorithm based on the H-K formula is proposed for image distortion correction using self-calibration. Three finite difference methods are used to approximate the Jacobian matrix, and the H-K formula is used to calculate the damping factor in each iteration. The Brown model, quadratic polynomial model and Fourier model are applied to the self-calibration, and the improved LM algorithm is used to solve the model parameters. In the simulation experiment of space resection of a single image, we evaluate the performance of the LM algorithm based on the gain ratio (LMh) and the improved LM algorithm based on the H-K formula (LMHK), and the accuracy of different models and algorithms is compared. A ridge trace analysis is carried out on the damping factor to illustrate the effects of the improved algorithm in handling ill-conditioning. In the second experiment, the improved algorithm is applied to measure the diameter of a coin using a single camera. The experimental results show that the improved LM algorithm can reach the same or higher accuracy as the LMh algorithm, and it can weaken the ill-conditioning to a certain extent and enhance the stability of the solution. Meanwhile, the applicability of the improved LM algorithm in self-calibration is verified.

Near-limit detonation in long spiral tube: An improved experimental methodology and frequency analysis

In the present work, near-limit detonations in near stoichiometric propane-oxygen mixtures were investigated using a long spiral tube facility. Compared to previous studies, our improvements in the tube length, framing rate and resolution, image processing methods for distortion correction and detonation front detection, allow for a significant increase of the amount and quality of data obtained. As the initial pressure was decreased, stable, stuttering, and galloping regimes of detonation propagation were successively observed. The use of wavelet transform made it possible for the first time to visualize the change of the intrinsic frequency of the oscillation of the detonation front velocity during the highly unstable propagation in the stuttering and galloping regimes. The failure/re-initiation cycles produce the highest power density at frequencies in the range of 500–1000 Hz. In the stuttering regime, additional velocity fluctuations were observed in the range 2000–12000 Hz during the re-initiation events. In the galloping regime, velocity fluctuations took place during the low-velocity periods, with characteristic frequencies in the range of 2000–2500 Hz. The dynamics of the failure and re-initiation events were characterized in terms of induction length normalized wavelength. It was shown that in the stuttering regime, the detonation failure events occur after a quite random length, whereas the detonation re-initiation events appear to occur after a rather constant length. In the galloping regime, less variability was observed, and failure and re-initiation events occur at a rather well defined frequency.Novelty and Significance Statement1. Improved experimental facility and techniques to study near-limit detonations in long spiral tube2. First application of wavelet transform to study near-limit detonations3. Detailed study of the frequency of failure and re-initiation events in the stuttering and galloping regimes

Multiple energy price distortions and improvement of potential energy consumption structure in the energy transition

Price distortion correction is considered an effective way to improve the energy consumption structure (ECS) in the era of carbon neutrality. However, the quantitative relationship between multiple energy price distortions and ECS has not been verified from the sector's perspective. Taking China's heavy industry as the research sample, we first estimate multiple energy price distortions (including coal, oil, and natural gas) from 2003 to 2019. Then, we employ the system generalized method of moments model to explore the impact of price distortion on ECS. On this basis, we further apply the input– output price and price- shifting models to analyze potential ECS improvement resulting from price distortion correction. The main results indicate the following: (1) The price distortion of natural gas is the most serious (− 25.54%), followed by oil (−15.99%) and coal (−7.25%). (2) The effects of absolute price distortion of coal and natural gas on ECS are positive, while oil price distortion negatively affects ECS. And relative price distortion for oil negatively affects ECS, while natural gas exerts a positive effect. (3) Removing the price distortion of coal and natural gas and slightly increasing the oil price will significantly improve the ECS without price control. Eliminating distortions of coal or natural gas individually under price controls would have a greater positive impact on ECS upgrade than that under non-price control. The results may offer policy support for the low-carbon transition of China's heavy industry.

Thin-slice reverse encoding distortion correction DWI facilitates visualization of non-functioning pituitary neuroendocrine tumor (PitNET)/pituitary adenoma and surrounding normal structures

BackgroundTo evaluate the clinical usefulness of thin-slice echo-planar imaging (EPI)-based diffusion-weighted imaging (DWI) with an on-console distortion correction technique, termed reverse encoding distortion correction DWI (RDC-DWI), in patients with non-functioning pituitary neuroendocrine tumor (PitNET)/pituitary adenoma.MethodsPatients with non-functioning PitNET/pituitary adenoma who underwent 3-T RDC-DWI between December 2021 and September 2022 were retrospectively enrolled. Image quality was compared among RDC-DWI, DWI with correction for distortion induced by B0 inhomogeneity alone (B0-corrected-DWI), and original EPI-based DWI with anterior-posterior phase-encoding direction (AP-DWI). Susceptibility artifact, anatomical visualization of cranial nerves, overall tumor visualization, and visualization of cavernous sinus invasion were assessed qualitatively. Quantitative assessment of geometric distortion was performed by evaluation of anterior and posterior displacement between each DWI and the corresponding three-dimensional T2-weighted imaging. Signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and apparent diffusion coefficient values were measured.ResultsSixty-four patients (age 70.8 ± 9.9 years [mean ± standard deviation]; 33 females) with non-functioning PitNET/pituitary adenoma were evaluated. In terms of susceptibility artifacts in the frontal and temporal lobes, visualization of left trigeminal nerve, overall tumor visualization, and anterior displacement, RDC-DWI performed the best and B0-corrected-DWI performed better than AP-DWI. The right oculomotor and right trigeminal nerves were better visualized by RDC-DWI than by B0-corrected-DWI and AP-DWI. Visualization of cavernous sinus invasion and posterior displacement were better by RDC-DWI and B0-corrected-DWI than by AP-DWI. SNR and CNR were the highest for RDC-DWI.ConclusionsRDC-DWI achieved excellent image quality regarding susceptibility artifact, geometric distortion, and tumor visualization in patients with non-functioning PitNET/pituitary adenoma.Relevance statementRDC-DWI facilitates excellent visualization of the pituitary region and surrounding normal structures, and its on-console distortion correction technique is convenient. RDC-DWI can clearly depict cavernous sinus invasion of PitNET/pituitary adenoma even without contrast medium.Key points• RDC-DWI is an EPI-based DWI technique with a novel on-console distortion correction technique.• RDC-DWI corrects distortion due to B0 field inhomogeneity and eddy current.• We evaluated the usefulness of thin-slice RDC-DWI in non-functioning PitNET/pituitary adenoma.• RDC-DWI exhibited excellent visualization in the pituitary region and surrounding structures.• In addition, the on-console distortion correction of RDC-DWI is clinically convenient.Graphical

Enhancing Single-Plane Fluoroscopy: A Self-Calibrating Bundle Adjustment for Distortion Modeling.

Single-plane fluoroscopy systems with image intensifiers remain commonly employed in a clinical setting. The imagery they capture is vulnerable to several types of geometric distortions introduced by the system's components and their assembly as well as interactions with the local and global magnetic fields. In this study, the application of a self-calibrating bundle adjustment is investigated as a method to correct geometric distortions in single-plane fluoroscopic imaging systems. The resulting calibrated imagery is then applied in the quantitative analysis of diaphragmatic motion and potential diagnostic applications to hemidiaphragm paralysis. The calibrated imagery is further explored and discussed in its potential impact on areas of surgical navigation. This work was accomplished through the application of a controlled experiment with three separate Philips Easy Diagnost R/F Systems. A highly redundant (~2500 to 3500 degrees-of-freedom) and geometrically strong network of 18 to 22 images of a low-cost target field was collected. The target field comprised 121 pre-surveyed tantalum beads embedded on a 25.4 mm × 25.4 mm acrylic base plate. The modeling process resulted in the estimation of five to eight distortion coefficients, depending on the system. The addition of these terms resulted in 83-85% improvement in terms of image point precision (model fit) and 85-95% improvement in 3D object reconstruction accuracy after calibration. This study demonstrates significant potential in enhancing the accuracy and reliability of fluoroscopic imaging, thereby improving the overall quality and effectiveness of medical diagnostics and treatments.

Automatic Recognition Technology of Library Books Based on Convolutional Neural Network Model

Background: The development of technological devices has changed many facets of our lives, particularly the way we engage with information and learning. The advent of automated technology for identification has had a had a revolutionary effect on how we read and organize books within the context of books and data searches. It starts by solving the difficulties in analyzing photos of book pages by using methods such as distortion rectification and book separation. Objective: The research compares the effectiveness of the suggested method with traditional straight-line identification techniques using real-world testing. Methodology: The Skip-Gram model in Word2Vec is used to accurately represent spoken language, allowing word vectors to be generated and input data to be preprocessed for CNN. The results show that the methodology created regarding the present investigation works better than alternatives concerning accuracy and efficiency during line identification. Result:This work advances the field of book suggestion systems by presenting a strong and effective method that leverages CNNs. The findings demonstrate deep learning techniques may be used to optimize system recommendations and improve customer service and happiness in a variety of contexts. This technique creates a bridge between natural language processing and picture evaluation and opens up new possibilities for suggestion advancement along with user satisfaction. Doi: 10.28991/HIJ-2024-05-01-015 Full Text: PDF

Knowledge atlas of white matter microstructure: a bibliometric analysis.

White matter microstructure is valued for being an indicator of neural network integrity, which plays an indispensable role in the execution of advanced brain functions. Although the number of publications has increased in the past 10 years, no comprehensive analysis has yet been conducted of this field. Therefore, this study aimed to identify the research hotspots and trends in research on white matter microstructure using a bibliometric analysis of the related literature published from 2013 to 2022. VOSviewer and CiteSpace were used to objectively analyze the research articles concerning white matter microstructure, which were retrieved from the Web of Science Core Collection (WoSCC). A total of 5,806 publications were obtained, with the number of published articles increasing annually over the past decade. The United States, China, the United Kingdom, and Canada maintained the top positions worldwide and had strong cooperative relationships. The top institution and journal were Harvard Medical School and Neuroimage, respectively. Alexander Leemans, Marek Kubicki, and Martha E Shenton were the most productive authors. Thematic keywords mainly included "diffusion tensor imaging" (DTI), "white matter integrity", and "connectivity". The keyword analysis revealed that DTI has a critical role in detecting white matter microstructure integrity and that fractional anisotropy is the main parameter in the assessment process. Keyword burst detection identified four research hotspots: movement, distortion correction, voxelwise analysis, and fixel-based analysis. This bibliometric analysis provided a systematic understanding of the research on white matter microstructure and identified the current frontiers. This may help clinicians and researchers comprehensively identify hotspots and trends in this field.

A correction method for radial distortion and nonlinear response of infrared cameras.

The key feature of non-contact temperature measurement provided by infrared (IR) cameras underpins their versatility. However, the accuracy of temperature measurements with IR cameras depends on imaging quality due to their non-contact nature, such as the lens, body temperature, and measurement environment. This paper addresses the correction of radial distortion and nonlinear response issues in IR cameras. To address radial distortion, we have designed a passive checkerboard calibration board specifically for infrared cameras. This board is used to calibrate the IR camera and derive the necessary camera parameters. Subsequently, these parameters are applied during the actual measurement process to rectify radial distortion effectively. Building on the radial distortion correction method mentioned above, we propose a multi-point segmented calibration approach that considers different temperature ranges and imaging regions. This method alleviates the issue of reduced temperature measurement accuracy due to variations in camera responses by computing gain and offset coefficient matrices for each temperature range. Experimental results demonstrate the effectiveness of the calibration board in correcting radial distortion in IR cameras, with a mean reprojection error of less than 0.16 pixels. Regarding the nonlinear response problem, the introduced method significantly reduces the relative error in temperature measurement. In the verification phase, spanning from 100 to 500 °C, the average relative error in temperature measurement decreases by 0.49% from 1.61% before and after correction, which highlights a substantial improvement in temperature measurement accuracy. This work gives a useful reference to improve the imaging quality and temperature measurement accuracy using infrared cameras.

Distortion map and correction for ultrasound localization microscopy imaging with a row–column array

The row–column array is an emerging technology which can enable large fields of view without the need for a large number of electronic channels. In ultrasound localization microscopy, sparse microbubbles are localized and tracked to produce high-resolution images of the microvasculature. During the localization of the microbubbles, it is typically assumed that the center of the target observed can be used as a substitute for the true microbubble location. However, the true microbubble will be located at the onset of the received signal. This distortion between the true location and the observed location will result in a shift of the microbubble away from the true location, thus altering the accuracy of the images produced. In most forms of ultrasound imaging, this shift mostly manifests as a vertical shift so can often be safely ignored. However, we have observed that in row–column array imaging this distortion will be spatially variable and will result in distortions in the axial, elevational, and lateral directions, which can be on the order of a few wavelengths. Consequently, this distortion, if ignored, will result in inaccuracies in the resulting ultrasound localization microscopy images. Here, we present a technique to map and correct for these distortions.

Spatial Transformation Accelerator with Parallel Data Access Scheme for Sample Reconstruction

Spatial image transformation is a commonly used component in many image processing pipelines. It enables the correction of optical distortions, image registration onto a common reference plane, electronic image stabilisation, digital zoom, video mosaicking, etc. With the growing tendency to embed image processing in low-power devices, attaining an efficient transformation solution becomes increasingly decisive. Furthermore, interpolation is the key operation in achieving the high quality of the transformed data from the original data. Fortunately, different implementations have already seen several efficiency improvements in recent years. However, interpolation relies on sampling a set of neighbouring points from memory, which has yet to be addressed efficiently for smaller computational platforms with limited memory resources. In this work, we derive a generic mathematical model and circuit design principles for the spatial transformation accelerator design for N-dimensional data. Furthermore, we present an efficient simultaneous access scheme for high-quality signal reconstruction. Finally, the introduced ideas are verified in field programmable gate arrays using one-dimensional and two-dimensional data transformation use cases. The presented solution is able to transform images with sizes ranging from 256 × 256 to 8192 × 8192 and achieves a transfer rate of 275 frames per second with 512 × 512 images.

Fly-scan high-throughput coded ptychographic microscopy via active micro-vibration and rolling-shutter distortion correction.

Recent advancements in ptychography have demonstrated the potential of coded ptychography (CP) for high-resolution optical imaging in a lensless configuration. However, CP suffers imaging throughput limitations due to scanning inefficiencies. To address this, we propose what we believe is a novel 'fly-scan' scanning strategy utilizing two eccentric rotating mass (ERM) vibration motors for high-throughput coded ptychographic microscopy. The intrinsic continuity of the 'fly-scan' technique effectively eliminates the scanning overhead typically encountered during data acquisition. Additionally, its randomized scanning trajectory considerably reduces periodic artifacts in image reconstruction. We also developed what we believe to be a novel rolling-shutter distortion correction algorithm to fix the rolling-shutter effects. We built up a low-cost, DIY-made prototype platform and validated our approach with various samples including a resolution target, a quantitative phase target, a thick potato sample and biospecimens. The reported platform may offer a cost-effective and turnkey solution for high-throughput bio-imaging.

New insights into experimental stratified flows obtained through physics-informed neural networks

We develop a physics-informed neural network (PINN) to significantly augment state-of-the-art experimental data of stratified flows. A fully connected deep neural network is trained using time-resolved experimental data in a salt-stratified inclined duct experiment, consisting of three-component velocity fields and density fields measured simultaneously in three dimensions at Reynolds number $= O(10^3)$ and at Prandtl or Schmidt number $=700$ . The PINN enforces incompressibility, the governing equations for momentum and buoyancy, and the boundary conditions at the duct walls. These physics-constrained, augmented data are output at an increased spatio-temporal resolution and demonstrate five key results: (i) the elimination of measurement noise; (ii) the correction of distortion caused by the scanning measurement technique; (iii) the identification of weak but dynamically important three-dimensional vortices of Holmboe waves; (iv) the revision of turbulent energy budgets and mixing efficiency; and (v) the prediction of the latent pressure field and its role in the observed asymmetric Holmboe wave dynamics. These results mark a significant step forward in furthering the reach of experiments, especially in the context of stratified turbulence, where accurately computing three-dimensional gradients and resolving small scales remain enduring challenges.

Enhancing Camera Calibration for Traffic Surveillance with an Integrated Approach of Genetic Algorithm and Particle Swarm Optimization.

Recent advancements in sensor technologies, coupled with signal processing and machine learning, have enabled real-time traffic control systems to effectively adapt to changing traffic conditions. Cameras, as sensors, offer a cost-effective means to determine the number, location, type, and speed of vehicles, aiding decision-making at traffic intersections. However, the effective use of cameras for traffic surveillance requires proper calibration. This paper proposes a new optimization-based method for camera calibration. In this approach, initial calibration parameters are established using the Direct Linear Transformation (DLT) method. Then, optimization algorithms are applied to further refine the calibration parameters for the correction of nonlinear lens distortions. A significant enhancement in the optimization process is achieved through the integration of the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) into a combined Integrated GA and PSO (IGAPSO) technique. The effectiveness of this method is demonstrated through the calibration of eleven roadside cameras at three different intersections. The experimental results show that when compared to the baseline DLT method, the vehicle localization error is reduced by 22.30% with GA, 22.31% with PSO, and 25.51% with IGAPSO.

An open relaxation-diffusion MRI dataset in neurosurgical studies

Diffusion MRI (dMRI) is a safe and noninvasive technique that provides insight into the microarchitecture of brain tissue. Relaxation-diffusion MRI (rdMRI) is an extension of traditional dMRI that captures diffusion imaging data at multiple TEs to detect tissue heterogeneity between relaxation and diffusivity. rdMRI has great potential in neurosurgical research including brain tumor grading and treatment response evaluation. However, the lack of available data has limited the exploration of rdMRI in clinical settings. To address this, we are sharing a high-quality rdMRI dataset from 18 neurosurgical patients with different types of lesions, as well as two healthy individuals as controls. The rdMRI data was acquired using 7 TEs, where at each TE multi-shell dMRI with high spatial and angular resolutions is obtained at each TE. Each rdMRI scan underwent thorough artifact and distortion corrections using a specially designed processing pipeline. The dataset’s quality was assessed using standard practices, including quality control and assurance. This resource is a valuable addition to neurosurgical studies, and all data are openly accessible.

Dynamic field mapping and distortion correction using single-shot blip-rewound EPI and joint multi-echo reconstruction.

To develop a method for dynamic mapping and distortion correction. A blip-rewound EPI trajectory was developed to acquire multiple 2D EPI images in a single readout with an interleaved order, which allows a short TE difference. A joint multi-echo reconstruction was utilized to exploit the shared information between EPI images. The reconstructed images from each readout are combined to produce a final magnitude image. A map is calculated from the phase of these images for distortion correction. The efficacy of the proposed method is assessed with phantom and in vivo experiments. The performance of the proposed method in the presence of subject motion is also investigated. Compared to conventional multi-echo EPI, the proposed method allows dynamic mapping at matched resolution with a much shorter TR. Phantom and in vivo results show that the proposed method can provide a comparable magnitude image as conventional single-shot EPI. The maps calculated from the proposed method are consistent with conventional multi-echo EPI in the phantom experiment. For in vivo experiments, the proposed method provides a more accurate estimation of than conventional multi-echo EPI, which is prone to phase wrapping problems due to the long TE difference. In-vivo scan with subject motion shows the proposed dynamic field mapping method can improve the temporal stability of EPI time series compared to gradient echo (GRE) based static field mapping. The proposed method allows accurate dynamic mapping for robust distortion correction without compromising spatial or temporal resolution.

Influence of preprocessing, distortion correction and cardiac triggering on the quality of diffusion MR images of spinal cord

Diffusion MRI of the spinal cord (SC) is susceptible to geometric distortion caused by field inhomogeneities, and prone to misalignment across time series and signal dropout caused by biological motion. Several modifications of image acquisition and image processing techniques have been introduced to overcome these artifacts, but their specific benefits are largely unproven and warrant further investigations. We aim to evaluate two specific aspects of image acquisition and processing that address image quality in diffusion studies of the spinal cord: susceptibility corrections to reduce geometric distortions, and cardiac triggering to minimize motion artifacts. First, we evaluate 4 distortion preprocessing strategies on 7 datasets of the cervical and lumbar SC and find that while distortion correction techniques increase geometric similarity to structural images, they are largely driven by the high-contrast cerebrospinal fluid, and do not consistently improve the geometry within the cord nor improve white-to-gray matter contrast. We recommend at a minimum to perform bulk-motion correction in preprocessing and posit that improvements/adaptations are needed for spinal cord distortion preprocessing algorithms, which are currently optimized and designed for brain imaging. Second, we design experiments to evaluate the impact of removing cardiac triggering. We show that when triggering is foregone, images are qualitatively similar to triggered sequences, do not have increased prevalence of artifacts, and result in similar diffusion tensor indices with similar reproducibility to triggered acquisitions. When triggering is removed, much shorter acquisitions are possible, which are also qualitatively and quantitatively similar to triggered sequences. We suggest that removing cardiac triggering for cervical SC diffusion can be a reasonable option to save time with minimal sacrifice to image quality.

Analysis of satellite-to-ground quantum key distribution with adaptive optics

Future quantum communication infrastructures will rely on both terrestrial and space-based links integrating high-performance optical systems engineered for this purpose. In space-based downlinks in particular, the loss budget and the variations in the signal propagation due to atmospheric turbulence effects impose a careful optimization of the coupling of light in single-mode fibers required for interfacing with the receiving stations and the ground networks. In this work, we perform a comprehensive study of the role of adaptive optics (AO) in this optimization, focusing on realistic baseline configurations of prepare-and-measure quantum key distribution, with both discrete and continuous-variable encoding, and including finite-size effects. Our analysis uses existing experimental turbulence datasets at both day and night time to model the coupled signal statistics following a wavefront distortion correction with AO, and allows us to estimate the secret key rate for a range of critical parameters, such as turbulence strength, satellite altitude and ground telescope diameter. The results we derive illustrate the interest of adopting advanced AO techniques in several practical configurations.

Method for Measuring the Three-Dimensional Morphology of Near-Wall Bubbles and Droplets Based on LED Digital Holography.

Digital holography, recognized for its noncontact nature and high precision in three-dimensional imaging, is effectively employed to measure the morphology of bubbles and droplets. However, in terms of near-wall bubbles and droplets, such as confined bubbles in microfluidic chips, the measurement of the interface morphology of bubbles near the glass surface has not yet been resolved due to the coherent noise resulting from glass surface reflections in microfluidic chips. Accordingly, an off-axis digital holography system was devised by using Linnik interferometry. Measuring the confined bubble interface near the wall within a microfluidic chip and droplet evaporation on solid surfaces was studied. Partially coherent LED sources and reference light modulation techniques were employed in the optical setup to mitigate the coherent noise. Dual exposure and weighted least-squares unwrapping algorithms were introduced to correct phase distortions, enhancing image quality. Imaging two confined CO2 bubbles was done near the wall in silicon oil within a porous microfluidic chip, and contact angles of 4.7 and 4.5° were measured. Additionally, the measurement of the three-dimensional morphology of vertically evaporating deionized water droplets on a glass surface was done, due to which calculation of contact angles at various orientations was possible. This work offers a feasible new method for measuring the 3D interface morphology of bubbles and droplets, particularly in microfluidic visualization, addressing current measurement gaps.