Low-Cost Implementation of Bilinear and Bicubic Image Interpolation for Real-Time Image Super-Resolution

Khaledyan, Donya; Amirany, Abdolah; Jafari, Kian; Moaiyeri, Mohammad Hossein; Khuzani, Abolfazl Zargari; Mashhadi, Najmeh

doi:10.1109/ghtc46280.2020.9342625

Cited by 36 publications

(8 citation statements)

References 23 publications

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“…Bilinear interpolation

(D_s^{bilinear})

. As depicted in Figure 2, the linear interpolation [33] is performed twice for the four adjacent pixel points along the plane coordinate axis to obtain the pixel value of the unknown point P . The output images do not have greyscale discontinuities and look smoother.…”

Section: Methodsmentioning

confidence: 99%

Medical image blind super‐resolution based on improved degradation process

Shao

Qin

Xiang

et al. 2023

IET Image Processing

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Clinical diagnosis has high requirements for the resolution of medical images, but most existing medical images super‐ resolution (SR) methods are performed under a known or specific degradation kernel. However, the difference between the actual degradations and their assumed degradation kernels results in a severe performance drop for the advanced SR methods in real applications. This paper proposes a medical image blind super‐resolution model (Med‐BSR) based on an improved degradation process to handle this issue. The model makes each of the degradation factors in medical image blind SR, such as blur, noise, and downsampling, more complex and practical. Specifically, the authors use the random select/combine strategy to randomly arrange and combine the type and order of each degradation factor, which significantly expands the degradation space. The authors also improved the loss function of the primary enhanced super‐resolution generative adversarial networks (ESRGAN) network. The extensive experimental results demonstrate that the authors’ designed model can accurately restore the natural degradation process, which can reconstruct high‐quality SR medical images. It also has a good generalization ability to realistic images simultaneously.

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“…Bilinear interpolation

(D_s^{bilinear})

Section: Methodsmentioning

confidence: 99%

Medical image blind super‐resolution based on improved degradation process

Shao

Qin

Xiang

et al. 2023

IET Image Processing

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show abstract

“…Ultrasound images can suffer from various artifacts, such as speckle noise, attenuation, and poor contrast, which can affect the quality of the image and make it difficult to interpret. Image enhancement techniques can be applied to remove or reduce these artifacts and improve the overall quality of the image [42,43]. DL models can learn to identify and classify features in images based on patterns in the pixel values.…”

Section: Data Enhancement and Augmentationmentioning

confidence: 99%

WATUNet: a deep neural network for segmentation of volumetric sweep imaging ultrasound

Khaledyan,

Marini,

O’Connell

et al. 2024

Mach. Learn.: Sci. Technol.

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Objective.Limited access to breast cancer diagnosis globally leads to delayed treatment. Ultrasound, an effective yet underutilized method, requires specialized training for sonographers, which hinders its widespread use. Approach. Volume sweep imaging (VSI) is an innovative approach that enables untrained operators to capture high-quality ultrasound images. Combined with deep learning, like convolutional neural networks (CNNs), it can potentially transform breast cancer diagnosis, enhancing accuracy, saving time and costs, and improving patient outcomes. The widely used UNet architecture, known for medical image segmentation, has limitations, such as vanishing gradients and a lack of multi-scale feature extraction and selective region attention. In this study, we present a novel segmentation model known as Wavelet_Attention_UNet (WATUNet). In this model, we incorporate wavelet gates (WGs) and attention gates (AGs) between the encoder and decoder instead of a simple connection to overcome the limitations mentioned, thereby improving model performance.Main results.Two datasets are utilized for the analysis. The public "Breast Ultrasound Images" (BUSI) dataset of 780 images and a VSI dataset of 3818 images. Both datasets contained segmented lesions categorized into three types: no mass, benign mass, and malignant mass. Our segmentation results show superior performance compared to other deep networks. The proposed algorithm attained a Dice coefficient of 0.94 and an F1 score of 0.94 on the VSI dataset and scored 0.93 and 0.94 on the public dataset, respectively. Significance. The experimental findings demonstrate that the proposed WATUNet model achieves precise segmentation of breast lesions in both standard-of-care and VSI images, surpassing state-of-the-art models. Hence, the model holds considerable promise for assisting in lesion identification, an essential step in the clinical diagnosis of breast lesions.

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“…Efficient image preprocessing is crucial before model application as it ensures the removal of irrelevant information, enhances image quality, and addresses artifacts, ultimately optimizing model performance by reducing noise and improving generalization [22,23]. At the beginning of the preprocessing stage, we removed non-image text and labels to streamline the dataset.…”

Section: Data Preprocessingmentioning

confidence: 99%

Developing an enhanced UNet-based architecture for breast tumor segmentation in ultrasound images

Khaledyan,

Marini,

O’Connell

et al. 2024

Medical Imaging 2024: Ultrasonic Imaging and Tomography

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Ultrasound imaging is a powerful imaging modality for diagnosing breast tumors due to its non-invasive nature, realtime imaging capabilities, and lack of ionizing radiation. Ultrasound imaging has certain limitations that can make it demanding to detect masses compared to other imaging modalities. Therefore, breast ultrasound image segmentation is a crucial and challenging task in computer-aided diagnosis (CAD) systems. Deep learning (DL) has revolutionized medical image segmentation. Among DL models, UNet architecture is widely used for its exceptional performance. This study assesses the effectiveness of sharpening filters and attention mechanisms between the decoder and encoder in UNet models for breast ultrasound segmentation. Combining Sharp UNet and Attention UNet, we propose a novel approach called Parallel Sharp Attention UNet (PSA_UNet). A public dataset of 780 cases was utilized in this study. The results are promising for the proposed method, with the Dice coefficient and F1 score of 0.93 and 0.94, respectively. McNemar's results show that our proposed model outperforms the earlier designs upon which our model is based. In addition to introducing a new network, this study highlights the importance of optimization and finetuning in improving UNet-based segmentation models. The results offer potential improvements in breast cancer diagnosis and treatment planning through more accurate and efficient medical image segmentation techniques.

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Low-Cost Implementation of Bilinear and Bicubic Image Interpolation for Real-Time Image Super-Resolution

Cited by 36 publications

References 23 publications

Medical image blind super‐resolution based on improved degradation process

Medical image blind super‐resolution based on improved degradation process

WATUNet: a deep neural network for segmentation of volumetric sweep imaging ultrasound

Developing an enhanced UNet-based architecture for breast tumor segmentation in ultrasound images

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