Existing Image Processing Methods Research Articles

Smart Ship Draft Reading by Dual-Flow Deep Learning Architecture and Multispectral Information.

In maritime transportation, a ship's draft survey serves as a primary method for weighing bulk cargo. The accuracy of the ship's draft reading determines the fairness of bulk cargo transactions. Human visual-based draft reading methods face issues such as safety concerns, high labor costs, and subjective interpretation. Therefore, some image processing methods are utilized to achieve automatic draft reading. However, due to the limitations in the spectral characteristics of RGB images, existing image processing methods are susceptible to water surface environmental interference, such as reflections. To solve this issue, we obtained and annotated 524 multispectral images of a ship's draft as the research dataset, marking the first application of integrating NIR information and RGB images for automatic draft reading tasks. Additionally, a dual-branch backbone named BIF is proposed to extract and combine spectral information from RGB and NIR images. The backbone network can be combined with the existing segmentation head and detection head to perform waterline segmentation and draft detection. By replacing the original ResNet-50 backbone of YOLOv8, we reached a mAP of 99.2% in the draft detection task. Similarly, combining UPerNet with our dual-branch backbone, the mIoU of the waterline segmentation task was improved from 98.9% to 99.3%. The inaccuracy of the draft reading is less than ±0.01 m, confirming the efficacy of our method for automatic draft reading tasks.

Lightweight Corn Leaf Detection and Counting Using Improved YOLOv8.

The number of maize leaves is an important indicator for assessing plant growth and regulating population structure. However, the traditional leaf counting method mainly relies on manual work, which is both time-consuming and straining, while the existing image processing methods have low accuracy and poor adaptability, making it difficult to meet the standards for practical application. To accurately detect the growth status of maize, an improved lightweight YOLOv8 maize leaf detection and counting method was proposed in this study. Firstly, the backbone of the YOLOv8 network is replaced using the StarNet network and the convolution and attention fusion module (CAFM) is introduced, which combines the local convolution and global attention mechanisms to enhance the ability of feature representation and fusion of information from different channels. Secondly, in the neck network part, the StarBlock module is used to improve the C2f module to capture more complex features while preserving the original feature information through jump connections to improve training stability and performance. Finally, a lightweight shared convolutional detection head (LSCD) is used to reduce repetitive computations and improve computational efficiency. The experimental results show that the precision, recall, and mAP50 of the improved model are 97.9%, 95.5%, and 97.5%, and the numbers of model parameters and model size are 1.8 M and 3.8 MB, which are reduced by 40.86% and 39.68% compared to YOLOv8. This study shows that the model improves the accuracy of maize leaf detection, assists breeders in making scientific decisions, provides a reference for the deployment and application of maize leaf number mobile end detection devices, and provides technical support for the high-quality assessment of maize growth.

Research on Intelligent Detection and Segmentation of Rock Joints Based on Deep Learning

The current methods for detecting joints on tunnel face rely primarily on manual sketches, which are associated with issues of low detection efficiency and subjectivity. To address these concerns, this paper presents an intelligent recognition and segmentation algorithm based on Mask R-CNN (mask region-based convolutional neural network) for detecting joint targets on tunnel face images and automatically segmenting them, thereby improving detection efficiency and objectivity of the results. Additionally, to tackle the challenge of low detection accuracy in existing image processing methods, particularly for complex tunnel joint surfaces in dark environments, the paper introduces a path aggregation network (PANet) to enhance the fusion capability of feature information in Mask R-CNN, thereby improving the accuracy of the intelligent detection method. The algorithm was trained on a dataset of 800 tunnel face images, and the research findings demonstrate that it can quickly detect the position of joints on tunnel face images and assign masks to the joint pixel regions to achieve joint segmentation. The mean average precision (mAP) of the detection boxes and segmentation in the 80 test set images were 58.0% and 49.2%, respectively, which outperforms the original Mask R-CNN algorithm and other intelligent recognition and segmentation algorithms.

Active visual continuous seam tracking based on adaptive feature detection and particle filter tracking

Welding seam tracking based on online programming is the future trend of intelligent production. However, most of the existing image processing methods have certain limitations in the adaptability, accuracy, and robustness of weld feature point detection. The online welding method of gas metal arc welding (GMAW) based on active vision sensing is studied in this paper. The Steger sub-pixel detection method is used to guarantee the accuracy of feature point extraction, and a self-adaptive search window and self-adaptive slope extraction are proposed on this basis. The self-adaptive window is generated according to the linear information of the weld area, and the scale factor and range threshold constraint are added to realize the real-time detection of the weld feature information. Screening the center pixel of the laser stripe in the self-adaptive window of the current frame by the initial slope or the self-adaptive slope of the previous frame, the linear information of the weld area is obtained. The self-adaptive slope of the current frame is fitted by the random sampling consistency method, and the pixel margin is retained to adapt to the linear detection of different continuous welds. When arc light and other serious interference make it difficult to obtain weld information, a particle filter is used to make the best prediction of the weld position. Finally, the welding robot platform based on laser vision sensing was built to test various continuous welds of the butt weld, fillet weld, and lap weld. Experimental results show that the detection speed is 27 ms, and the accuracy of detection and tracking can respectively reach 0.03 mm and 0.78 mm, which meets the requirements of weld detection and tracking.

Digital variance angiography in patients undergoing lower limb arterial recanalization: cost-effectiveness analysis within the English healthcare setting.

Aim: Digital variance angiography (DVA) is a recently developed image processing method capable of improving image quality compared with the traditionally used digital subtraction angiography (DSA), among patients undergoing lower limb x-ray angiography. This study aims to explore the potential cost-effectiveness of DVA from an English National Health Service perspective. Materials & methods: A two-part economic model, consisting of a decision tree and a Markov model, was developed to consider the costs and health outcomes associated with the use of DVA as part of current practice imaging, compared with x-ray angiography using standard DSA. The model explored the impact of DVA on the development of acute kidney injury (AKI), chronic kidney disease and radiation-induced cancer over a lifetime horizon. Both deterministic and probabilistic analyses were performed to assess the cost per quality-adjusted life-year (QALY). Results: Base-case results indicate that DVA results in cost savings of £309 per patient, with QALYs also improving (+0.025) over a lifetime. As shown in sensitivity analysis, a key driver of model results is the relative risk (RR) reduction of contrast-associated acute kidney injury associated with use of DVA. The intervention also decreases the risk of carcinoma over a lifetime. Scenario analyses show that cost savings range from £310 to £553, with QALY gains ranging from 0.048 to 0.109 per patient. Conclusion: The use of DVA could result in a decrease in costs and an increase in QALYs over a lifetime, compared with existing imaging practice. The potential for this technology to offer an economically viable alternative to existing image processing methods, through a reduction in contrast media volume and radiation exposure, has been demonstrated.

Analysis and improvement of methods and means for eliminating distortions in image and video signals

The main purpose of this article was to study existing image processing methods, binarization and noise reduction, and to develop an improved adaptive thresholding algorithm. The analysis of methods and tools for eliminating distortions in image and video signals is an urgent task in numerous fields, including medicine, computer vision, and document science. The article discusses in detail the existing binarization and noise reduction methods, highlighting their advantages and limitations. However, the main achievement is the development and implementation of an improved adaptive thresholding algorithm. This algorithm considers the specific features of the image and automatically adapts the binarization threshold for better processing quality. It is a significant contribution to the field of image processing and can be used in various fields, including medical diagnostics and visual object detection in images.

Full-field deformation measurement of structural nodes based on panoramic camera and deep learning-based tracking method

Full-field deformation measurement of structures generally requires the aid of complex and expensive multi-camera measurement systems. A full-field structural deformation measurement method using a single panoramic camera and deep learning-based tracking algorithm is proposed. The contributions are as follows: (1) To address the problem that existing full-field image acquisition methods rely on multi-camera systems, a full-field image acquisition method based on a single panoramic camera is proposed, in which a distortion-free planar image covering the full-field of the structure is obtained by decomposing the projection in multiple directions based on the panoramic camera imaging model and the cubic projection method. (2) To solve the problem that the nodes of structures usually contain little texture and are difficult to track robustly with existing image processing methods, an object detection network with a modified tiny feature map layer and attention mechanism is applied to extract the region of interest (ROI) of each node automatically. (3) Finally, the ROIs of the identified nodes are clustered using a perceptual hashing method and then the node coordinates are calculated using a line segment detector (LSD) and intersection fitting. The proposed method is validated on a scaled model of a stadium, and the comparison to the deformation results of total station shows that the proposed method can calculate the displacement of all node at once, and the average error between the displacement results and those of the total station is 3.7 mm, which proves the practicality of the proposed method.

EBAT: Enhanced Bidirectional and Autoregressive Transformers for Removing Hairs in Hairy Dermoscopic Images

A great progress in deep learning technologies for skin cancer detection from dermoscopic images has been made for a decade. While its performance is vulnerable to a large amount of hairs densely covering the skin surface, the existing image processing methods frequently fail to remove hairs in hairy skin images. In this paper, we propose, as a deep learning approach to removing hairs, a generative image inpainting network where bidirectional autoregressive transformers (BATs) are employed to learn image features and are systematically integrated with convolutional neural networks (CNNs) in multiple spatial scales in order to reconstruct missing regions. Each patch split from a masked image is unfolded and processed through BATs, and re-folded to constitute diverse shapes of feature maps through kernel-based unfolding-folding operations. By introducing the multi-scale features extracted by collaborative learning of transformers and CNNs to the texture generator network, our method can effectively reconstruct minute details of local regions as well as global structure which might not be easily inferred from neighbor pixels in hairy skin images. Quantitative and qualitative evaluations show not only that our multi-scale dual-modality strategy is much robust to reconstruct hair-shaped missing regions compared to the existing transformer-based image inpainting method called BAT-Fill, but also that our framework outperforms the state-of-the-art image inpainting models in removing hairs from hairy dermoscopic images.

Exploring Simple and Transferable Recognition-Aware Image Processing.

Recent progress in image recognition has stimulated the deployment of vision systems at an unprecedented scale. As a result, visual data are now often consumed not only by humans but also by machines. Existing image processing methods only optimize for better human perception, yet the resulting images may not be accurately recognized by machines. This can be undesirable, e.g., the images can be improperly handled by search engines or recommendation systems. In this work, we examine simple approaches to improve machine recognition of processed images: optimizing the recognition loss directly on the image processing network or through an intermediate input transformation model. Interestingly, the processing model's ability to enhance recognition quality can transfer when evaluated on models of different architectures, recognized categories, tasks, and training datasets. This makes the methods applicable even when we do not have the knowledge of future recognition models, e.g., when uploading processed images to the Internet. We conduct experiments on multiple image processing tasks paired with ImageNet classification and PASCAL VOC detection as recognition tasks. With these simple yet effective methods, substantial accuracy gain can be achieved with strong transferability and minimal image quality loss. Through a user study we further show that the accuracy gain can transfer to a black-box cloud model. Finally, we try to explain this transferability phenomenon by demonstrating the similarities of different models' decision boundaries. Code is available at https://github.com/liuzhuang13/Transferable_RA.

Automatic Quantification of Atherosclerosis in Contrast-Enhanced MicroCT Scans of Mouse Aortas Ex Vivo.

Objective While microCT evaluation of atherosclerotic lesions in mice has been formally validated, existing image processing methods remain undisclosed. We aimed to develop and validate a reproducible image processing workflow based on phosphotungstic acid-enhanced microCT scans for the volumetric quantification of atherosclerotic lesions in entire mouse aortas. Approach and Results. 42 WT and 42 apolipoprotein E knockout mouse aortas were scanned. The walls, lumen, and plaque objects were segmented using dual-threshold algorithms. Aortic and plaque volumes were computed by voxel counting and lesion surface by triangulation. The results were validated against manual and histological evaluations. Knockout mice had a significant increase in plaque volume compared to wild types with a plaque to aorta volume ratio of 0.3%, 2.8%, and 9.8% at weeks 13, 18, and 26, respectively. Automatic segmentation correlated with manual (r2 ≥ 0.89; p < .001) and histological evaluations (r2 > 0.96; p < .001). Conclusions The semiautomatic workflow enabled rapid quantification of atherosclerotic plaques in mice with minimal manual work.

Research on image super-resolution algorithm based on mixed deep convolutional networks

The existing image processing methods had aimed at the problems of blurred image reconstruction, large noise, and poor visual perception. The improved image super-resolution algorithm based on mixed deep convolutional networks is proposed in the paper. Firstly, the proposed method can shrink the low-resolution image to the specified size in upsampling phase. Secondly, it can extract features from low-resolution images. It sends the extracted initial features into the convolutional coding and decoding structure for image features. Thirdly, the feature extraction and calculation in high-dimensions are performed using dilated convolution in reconstruction layer. The high-resolution image had been reconstructed. The proposed method had been compared with state-of-arts on Set5, Set14, BSD100, and Urban100 datasets. The experimental results can show that the Peak Signal-to-Noise Ratio is increased by some ranges, and the Structural Similarity is increased by some effective percentage points.

Deep learning optimization method for counting overlapping rice seeds

AbstractOverlapping seeds counting method is an important direction to improve the detection efficiency of rice thousand kernel weight, but the existing image processing methods may misjudge the overlapping distribution when the thin‐layer seeds are piled up. In this study, we proposed a deep learning optimization method based on contour grouping pre‐labeling for counting overlapping rice seeds. First, the contour grouping method—based on the Euclidean distance and divergence function as comprehensive criteria—pre‐labels the rice seed contours to reduce the number of potential false classification edge points. The purpose of integrating the pre‐labeling results in its feature extraction layer and using a linear combination of inter‐class and intra‐class error functions as the total is to improve the accuracy of deep learning. Second, based on the improved Faster region‐convolutional neural network method, the overlapping rice seeds were accurately counted. An orthogonal test is proposed to optimize the full connection layer's parameter configuration, aimed at improving the learning speed. The experimental results demonstrate that the average error rate of rice seeds in a single image (960 × 960 pixels) is 1.06% and the average recognition time of counting (≤400 seeds) was 0.45 s. The proposed method can be used as an effective tool for counting overlapping rice or other crop seeds.Practical ApplicationCurrently, the overlapping distributions of rice seeds may be misjudged when the thin seed layer is stacked for determining the thousand kernel weight and the deep learning optimization method in this study can be used as an effective tool for counting overlapping rice or other crop seeds.

Automatic Detection of Genetics and Genomics of Eye Disease Using Deep Assimilation Learning Algorithm.

Diabetic retinopathy (DR) is one of the most prevalent genetic diseases in human and it is caused by damage to the blood vessels in the eye retina. If it is undetected and untreated at right time, it can lead to vision loss. There are many medical imaging and processing technologies to improve the diagnostic process of DR to overcome the lack of human experts. In the existing image processing methods, there are issues such as lack of noise removal, improper clustering segmentation and less classification accuracy. This can be accomplished by automatic diagnosis of DR using advanced image processing method. The cotton wool spot (CWS), hard exudates (HE) contains a common manifestation of many diseases in retina including DR and acquired immunodeficiency syndrome. In the present work, super iterative clustering algorithm (SICA) is proposed to identify the CWS, HE on retinal image. Feature-based medical image retrieval (FBMIR) datasets are utilized for this purpose. Noises present on the images and histogram-filtering technique is used to convert red, green, and blue (RGB) images into a perfect greyscale image without noise. After pre-processing, SICA is used to identify the CWS, HE detection on retinal images and eliminates unnecessary areas of interest. In the third stage, after detecting CWS and HE, various statistical features are extracted for further classification using deep assimilation learning algorithm (DALA). The performance of DALA technique is examined with various classification parameters like recall, precision, and F-measure. Finally, the false classification ratios are computed to compare the performance of the trained networks. The proposed method produces accurate detection of affected regions with an accuracy ratio of 98.5% and it is higher than the other conventional methods. This method may improve the accuracy of automatic detection and classification of eye diseases.

Optimization of Underwater Marker Detection Based on YOLOv3

The research on the detection and recognition technology of marker is of great significance for some underwater operations, such as marine resource exploration, underwater robot operation and so on. The existing image processing methods can effectively detect and recognize the markers in the air. Nevertheless, in the underwater environment, the complex imaging environment of the ocean leads to serious degradation of underwater images obtained by the optical vision system. Due to the lack of effective information for object recognition, the severely degraded underwater images increases the difficulty of detection and recognition of underwater objects. With the development of high-tech underwater imaging equipment, the quality of underwater images has been improved to a certain extent, but there are still some phenomena such as color fading, low contrast and blurred details. Solutions to overcome these problems are important for the exploration of the ocean. In this paper, we introduce a deep learning model to optimize the performance of detection, and make a unique marker dataset for the application scene of our experiment. We first use the deep learning network to pre-train the marker images in the air. Next, we use the underwater marker images for fine-tuning. Finally, after the target marker is detected, the traditional image processing method is used to recognize the marker. Experimental results show that the optimization method we proposed achieves better performance on the dataset.

A unified framework for packing deformable and non-deformable subcellular structures in crowded cryo-electron tomogram simulation

BackgroundCryo-electron tomography is an important and powerful technique to explore the structure, abundance, and location of ultrastructure in a near-native state. It contains detailed information of all macromolecular complexes in a sample cell. However, due to the compact and crowded status, the missing edge effect, and low signal to noise ratio (SNR), it is extremely challenging to recover such information with existing image processing methods. Cryo-electron tomogram simulation is an effective solution to test and optimize the performance of the above image processing methods. The simulated images could be regarded as the labeled data which covers a wide range of macromolecular complexes and ultrastructure. To approximate the crowded cellular environment, it is very important to pack these heterogeneous structures as tightly as possible. Besides, simulating non-deformable and deformable components under a unified framework also need to be achieved.ResultIn this paper, we proposed a unified framework for simulating crowded cryo-electron tomogram images including non-deformable macromolecular complexes and deformable ultrastructures. A macromolecule was approximated using multiple balls with fixed relative positions to reduce the vacuum volume. A ultrastructure, such as membrane and filament, was approximated using multiple balls with flexible relative positions so that this structure could deform under force field. In the experiment, 400 macromolecules of 20 representative types were packed into simulated cytoplasm by our framework, and numerical verification proved that our method has a smaller volume and higher compression ratio than the baseline single-ball model. We also packed filaments, membranes and macromolecules together, to obtain a simulated cryo-electron tomogram image with deformable structures. The simulated results are closer to the real Cryo-ET, making the analysis more difficult. The DOG particle picking method and the image segmentation method are tested on our simulation data, and the experimental results show that these methods still have much room for improvement.ConclusionThe proposed multi-ball model can achieve more crowded packaging results and contains richer elements with different properties to obtain more realistic cryo-electron tomogram simulation. This enables users to simulate cryo-electron tomogram images with non-deformable macromolecular complexes and deformable ultrastructures under a unified framework. To illustrate the advantages of our framework in improving the compression ratio, we calculated the volume of simulated macromolecular under our multi-ball method and traditional single-ball method. We also performed the packing experiment of filaments and membranes to demonstrate the simulation ability of deformable structures. Our method can be used to do a benchmark by generating large labeled cryo-ET dataset and evaluating existing image processing methods. Since the content of the simulated cryo-ET is more complex and crowded compared with previous ones, it will pose a greater challenge to existing image processing methods.

Garbage Sorting System Based on Composite Layer CNN and Multi-Robots

Object detection technology is the key problem in the garbage sorting system. There are many difficulties in identifying the target in the garbage, such as poor visibility and targets being coverage. The existing image processing methods are difficult to detect the target objects in the garbage sorting system. To solve this problem, this paper proposes a garbage sorting system based on convolution neural network and robots. The CNN network combines the features of three different network layers and predicts the location and angle information of the target objects at the same time, which we called composite layer. Coupled with the training solution of classification regression and angle prediction, the performance of object detection is further improved. The entire sorting system is composed of three subsystems: vision system, electrical system and mechanical system. The vision system is responsible for the image processing and send the position and attitude information to the electrical system. The electrical system controls the different robots to do sorting motion. We construct a TestSet which has 23983 objects to be detected. The experiment results show that our system’s Drop rate is 1.7% and False rate is 4.8%, which is good enough to meet the needs.

Enhancement in Quality Estimation of Resistance Spot Welding Using Vision System and Fuzzy Support Vector Machine

The current nondestructive testing methods such as ultrasonic, magnetic, or eddy current signals, and even the existing image processing methods, present certain challenges and show a lack of flexibility in building an effective and real-time quality estimation system of the resistance spot welding (RSW). This paper provides a significant improvement in the theory and practices for designing a robotized inspection station for RSW at the car manufacturing plants using image processing and fuzzy support vector machine (FSVM). The weld nuggets’ positions on each of the used car underbody models are detected mathematically. Then, to collect perfect pictures of the weld nuggets on each of these models, the required end-effector path is planned in real-time by establishing the Denavit-Hartenberg (D-H) model and solving the forward and inverse kinematics models of the used six-degrees of freedom (6-DOF) robotic arm. After that, the most frequent resistance spot-welding failure modes are reviewed. Improved image processing methods are employed to extract new features from the elliptical-shaped weld nugget’s surface and obtain a three-dimensional (3D) reconstruction model of the weld’s surface. The extracted artificial data of thousands of samples of the weld nuggets are divided into three groups. Then, the FSVM learning algorithm is formed by applying the fuzzy membership functions to each group. The improved image processing with the proposed FSVM method shows good performance in classifying the failure modes and dealing with the image noise. The experimental results show that the improvement of comprehensive automatic real-time quality evaluation of RSW surfaces is meaningful: the quality estimation could be processed within 0.5 s in very high accuracy.

Mu-net: Multi-scale U-net for two-photon microscopy image denoising and restoration

Advances in two two-photon microscopy (2PM) have made three-dimensional (3D) neural imaging of deep cortical regions possible. However, 2PM often suffers from poor image quality because of various noise factors, including blur, white noise, and photo bleaching. In addition, the effectiveness of the existing image processing methods is limited because of the special features of 2PM images such as deeper tissue penetration but higher image noises owing to rapid laser scanning. To address the denoising problems in 2PM 3D images, we present a new algorithm based on deep convolutional neural networks (CNNs). The proposed model consists of multiple U-nets in which an individual U-net removes noises at different scales and then yields a performance improvement based on a coarse-to-fine strategy. Moreover, the constituent CNNs employ fully 3D convolution operations. Such an architecture enables the proposed model to facilitate end-to-end learning without any pre/post processing. Based on the experiments on 2PM image denoising, we observed that our new algorithm demonstrates substantial performance improvements over other baseline methods.

Ultrafast Plane Wave Imaging With Line-Scan-Quality Using an Ultrasound-Transfer Generative Adversarial Network.

In the medical ultrasound field, ultrafast imaging has recently become a hot topic. However, the diagnostic reliability of ultrafast high-frame rate plane-wave (PW) imaging is reduced by its low-quality images. The medical ultrasound equipment on the market usually adopts the line-scanning mode, which can obtain high-quality images at a very low frame rate. In addition, many proven data-driven ultrasound image processing methods are trained by line-scan images. Since the gray-level distributions of line-scan images and PW images are very different, these gray-level distribution-sensitive methods cannot be generalized to ultrafast ultrasound imaging, which limits further applications. Hence, we propose an ultrasound-transfer generative adversarial network to improve the quality of PW images and extend the existing image processing methods to ultrafast ultrasound imaging by reconstructing PW images into line-scan images. This network adopts a residual dense generator with a self-attention system that fully uses the hierarchical features and generates details from all the relevant physiological information. A projection discriminator and spectral normalization are introduced to increase the discernibility and to maintain a balance between the generator and the discriminator. Moreover, we reorganize the transmit sequence of the transducer array to eliminate the negative influence of human movements and facilitate the convergence of the proposed model. The experimental results are evaluated with five metrics, which confirm the feasibility of the proposed method to obtain a line-scan-quality image with a very high frame rate. This technology could significantly popularize ultrafast medical ultrasound imaging.

An improved image processing technique for determination of volume and surface area of rising bubble

Bubble volume and bubble surface area are key parameters that affect the working performance of bubble columns. Due to their non-intrusive characteristics and high spatial resolution, image processing techniques can be used to measure bubble volume. However, bubbles in practical application are not always perfectly spherical or ellipsoidal. In homogenous flow regimes where bubble interaction is weak, the bubble geometry can be classified into three types: round cap, ellipsoidal, and disk-shaped. For large and severely distorted bubbles, it is difficult to employ existing image processing methods for measuring the bubble volume. This study describes an image analysis technique that was developed to obtain the volume and surface area of large and severely deformed bubbles. This proposed image processing method is based on a differential segmentation concept and possesses better adaptability because it considers both the bubble geometry and deflection angle of the bubble. The developed image processing technique is verified using the results of a bubble collection experiment. By making use of the developed image processing algorithm, the volume and surface area of discrete bubbles have been calculated. The relationship between bubble volume distribution and gas flow rate is discussed and the characteristics of bubble surface area variance are shown. Moreover, the influences of bubble volume and surface area on the bubble rise velocity are analyzed.