A study on image processing using mathematical morphological

Said, Khairul Anuar Mat; Jambek, Asral Bahari

doi:10.1109/iced.2016.7804697

Cited by 22 publications

(12 citation statements)

References 15 publications

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“…This is known as morphological opening, and it removes small objects (noise) from an image while preserving the shape and size of larger objects in the image. Figure 4 shows an example of a morphological opening that removes the noisy patches from the original image (Said et al., 2016). The resulting binary image contains a non‐zero value only if the structuring element morphological tests are successful at a location in the input image.…”

Section: Methodology and Data Sourcesmentioning

confidence: 99%

Spatiotemporal Data Augmentation of MODIS‐Landsat Water Bodies Using Adversarial Networks

Filali Boubrahimi,

Neema,

Nassar

et al. 2024

Water Resources Research

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With increasing demands for precise water resource management, there is a growing need for advanced techniques in mapping water bodies. The currently deployed satellites provide complementary data that are either of high spatial or high temporal resolutions. As a result, there is a clear trade‐off between space and time when considering a single data source. For the efficient monitoring of multiple environmental resources, various Earth science applications need data at high spatial and temporal resolutions. To address this need, many data fusion methods have been described in the literature, that rely on combining data snapshots from multiple sources. Traditional methods face limitations due to sensitivity to atmospheric disturbances and other environmental factors, resulting in noise, outliers, and missing data. This paper introduces Hydrological Generative Adversarial Network (Hydro‐GAN), a novel machine learning‐based method that utilizes modified GANs to enhance boundary accuracy when mapping low‐resolution MODIS data to high‐resolution Landsat‐8 images. We propose a new non‐saturating loss function for the Hydro‐GAN generator, which maximizes the log of discriminator probabilities to promote stable updates and aid convergence. By focusing on reducing squared differences between real and synthetic images, our approach enhances training stability and overall performance. We specifically focus on mapping water bodies using MODIS and Landsat‐8 imagery due to their relevance in water resource management tasks. Our experimental results demonstrate the effectiveness of Hydro‐GAN in generating high‐resolution water body maps, outperforming traditional methods in terms of boundary accuracy and overall quality.

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Section: Methodology and Data Sourcesmentioning

confidence: 99%

Spatiotemporal Data Augmentation of MODIS‐Landsat Water Bodies Using Adversarial Networks

Filali Boubrahimi,

Neema,

Nassar

et al. 2024

Water Resources Research

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“…Classification is to extract the features of the segmented targets and judge the class of the targets according to their features. The steps can be summarized as follows: (1) input the in-situ image and adjust the brightness; (2) operate denoising and edge enhancement; (3) implement the threshold segmentation proposed by (Otsu, 1979) based on maximum between-cluster variance to finish binarization; (4) demonstrate the morphological closing operation (Said et al, 2016) to fill the discontinuities, holes, and edge breaks; (5) implement the contour extraction based on boundary tracking (Suzuki, 1985;Marini et al, 2018) to obtain the regions of interest (ROIs) of the targets; (6) classify the detected targets using the selected calculation model; and (7) operate statistics of the quantity and species of plankton. The contributions of PPD method and Bilateral-Sobel edge enhancement are in steps ( 6) and (2), respectively.…”

Section: Methodsmentioning

confidence: 99%

Toward efficient deep learning system for in-situ plankton image recognition

Yue,

Chen,

Long

et al. 2023

Front. Mar. Sci.

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Plankton is critical for the structure and function of marine ecosystems. In the past three decades, various underwater imaging systems have been developed to collect in-situ plankton images and image processing has been a major bottleneck that hinders the deployment of plankton imaging systems. In recent years, deep learning methods have greatly enhanced our ability of processing in-situ plankton images, but high-computational demands and longtime consumption still remain problematic. In this study, we used knowledge distillation as a framework for model compression and improved computing efficiency while maintaining original high accuracy. A novel inter-class similarity distillation algorithm based on feature prototypes was proposed and enabled the student network (small scale) to acquire excellent ability for plankton recognition after being guided by the teacher network (large scale). To identify the suitable teacher network, we compared emerging Transformer neural networks and convolution neural networks (CNNs), and the best performing deep learning model, Swin-B, was selected. Utilizing the proposed knowledge distillation algorithm, the feature extraction ability of Swin-B was transferred to five more lightweight networks, and the results had been evaluated in taxonomic dataset of in-situ plankton images. Subsequently, the chosen lightweight model and the Bilateral–Sobel edge enhancement were tested to process in-situ images with high level of noises captured from coastal waters of Guangdong, China and achieved an overall recall rate of 91.73%. Our work contributes to effective deep learning models and facilitates the deployment of underwater plankton imaging systems by promoting both accuracy and speed in recognition of plankton targets.

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“…We adjust the window size of 3 × 3 to define the input to the filter function that substitutes the central pixel with the darkest one in the running window. It also eliminates the positive external noise existing in the OCT digital image 18 …”

Section: Implementation Methodologiesmentioning

confidence: 99%

Deep learning based automated detection of intraretinal cystoid fluid

Ahmed

Panhwar

Baqai

et al. 2021

Int J Imaging Syst Tech

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The diversified ocular disorders in which cystoid macular edema (CME) occurs, are strongly associated with the vision loss. Optical coherence tomography (OCT) scans that allow screening of the retina, contain artifacts including blur‐edges, speckle noise, and so forth, which create difficulty in identifying retinal fluid. In this work, major image preprocessing techniques such as minimum filtering, block‐matching and 3D filtering, and Richardson–Lucy deconvolution method are applied to minimize noise and other degradation effects from OCT scans, ensured by maintaining image quality assessment scores, that is, blind‐less image spatial quality evaluator and sharpness estimation score. Furthermore, this work develops an automated method based on deep learning (DL) to detect the presence and progression of retinal fluid, that is, CME and its quantification. Specifically, U‐net model with efficient tuning of hyperparameters is proposed to obtain optimal results. The algorithm's performance is evaluated for accurate identification of fluid localization in the case of diabetic macular edema (DME), having CME regions, and compared against the manual segmentation of fluid by experts, which are highly correlated. This enhanced DL method achieves better performance than the algorithms previously reported. The proposed algorithm is evaluated on several performance metrics during training and reports the best score of 99.81, 0.50, 83.34, 86.56, 80.89, and 99.48% for model accuracy, binary cross‐entropy loss, Dice coefficient (DC), precision, recall, and area under the ROC‐curve, respectively. Additionally, fourfold cross‐validation technique is also applied and an average DC score of 84.00% is obtained. This approach can be helpful to prevent vision loss.

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A study on image processing using mathematical morphological

Cited by 22 publications

References 15 publications

Spatiotemporal Data Augmentation of MODIS‐Landsat Water Bodies Using Adversarial Networks

Spatiotemporal Data Augmentation of MODIS‐Landsat Water Bodies Using Adversarial Networks

Toward efficient deep learning system for in-situ plankton image recognition

Deep learning based automated detection of intraretinal cystoid fluid

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