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Defocus blur detection using novel local directional mean patterns (LDMP) and segmentation via KNN matting
Detection and segmentation of defocus blur is a challenging task in digital imaging applications as the blurry images comprise of blur and sharp...
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Defocus blur detection based on transformer and complementary residual learning
Defocus blur detection (DBD), a technique for detecting defocus or in-focus pixels in a single image, has been widely used in various fields....
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Segmentation of Intraoperative 3D Ultrasound Images Using a Pyramidal Blur-Pooled 2D U-Net
Automatic localization and segmentation of the tumor and resection cavity in intraoperative ultrasound images can assist in accurate navigation...
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Image enhancement and blur pixel identification with optimization-enabled deep learning for image restoration
Image enhancement is the process of enhancing specific aspects of an image, such as its borders or contrast. The procedure of restoring a destroyed...
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A single defocused image depth recovery with superpixel segmentation
Most of the depth restoration algorithms are complicated for a single defocused image, the restoration effect is poor on the image edges, complex...
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Tuberculosis mycobacterium segmentation using deeply connected membership tweaked fuzzy segmentation network
Tuberculosis (TB) is a contagious disease that spreads through the air when an infected person coughs, sneezes, or talks. TB is a bacterial infection...
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Improving Skin Lesion Diagnosis: Hybrid Blur Detection for Accurate Dermatological Image Analysis
Accurate diagnosis of skin lesions is crucial for early detection and effective treatment of dermatological conditions. However, blurry artifacts...
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Defocus Blur detection via transformer encoder and edge guidance
Defocus blur detection (DBD) aims to separate blurred and unblurred regions for a given image. Benefiting from the powerful extraction capabilities...
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Neural Networks for Classification and Unsupervised Segmentation of Visibility Artifacts on Monocular Camera Image
AbstractFor computer vision systems of autonomous vehicles, an important task is to ensure high reliability of visual information coming from...
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Defocus Blur Synthesis and Deblurring via Interpolation and Extrapolation in Latent Space
Though modern microscopes have an autofocusing system to ensure optimal focus, out-of-focus images can still occur when cells within the medium are...
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OV-VIS: Open-Vocabulary Video Instance Segmentation
Conventionally, the goal of Video Instance Segmentation (VIS) is to segment and categorize objects in videos from a closed set of training...
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Instance Segmentation in the Dark
Existing instance segmentation techniques are primarily tailored for high-visibility inputs, but their performance significantly deteriorates in...
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Towards Diverse Binary Segmentation via a Simple yet General Gated Network
In many binary segmentation tasks, most CNNs-based methods use a U-shape encoder-decoder network as their basic structure. They ignore two key...
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Full-duplex strategy for video object segmentation
Previous video object segmentation approaches mainly focus on simplex solutions linking appearance and motion, limiting effective feature...
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Local feature driven fuzzy local information C-means clustering with kernel metric for blurred and noisy image segmentation
Kernel fuzzy weighted local information C-means clustering is a widely used robust segmentation algorithm for noisy images. However, it cannot...
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Improved Brain Tumor Segmentation Using UNet-LSTM Architecture
Brain Tumor is always known for its deadliest behavior and people’s less survival probability against it. It is a complex and life- changing medical...
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Rainy day image semantic segmentation based on two-stage progressive network
Semantic segmentation plays a crucial role in the fields of computer vision and computer graphics, with extensive applications in various practical...
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Benchmarking the Robustness of LiDAR Semantic Segmentation Models
When using LiDAR semantic segmentation models for safety-critical applications such as autonomous driving, it is essential to understand and improve...
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MFFLNet: lightweight semantic segmentation network based on multi-scale feature fusion
Semantic segmentation is a typical problem in the field of machine vision. Convolutional neural networks(CNNs)-based methods all have excellent...
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TongueMobile: automated tongue segmentation and diagnosis on smartphones
Tongue diagnosis is a useful process in traditional Chinese medicine to assess diseases non-invasively by visually inspecting the tongue and its...
