Nighttime driving poses visibility challenges, but image translation methods can help by transforming night images into day-like scenes. The Cycle-GAN is a versatile unpaired image translation model which can easily b...
Nighttime driving poses visibility challenges, but image translation methods can help by transforming night images into day-like scenes. The Cycle-GAN is a versatile unpaired image translation model which can easily be adept to night-to-day image translation tasks. However, it generates unnatural and unrealistic outcomes in these cases. This study is focused on addressing this with a novel training strategy for the Cycle-GAN, employing a tailored training objective that incorporates perceptual quality optimization. This training objective aims to boost the naturalness and perceptual quality of the generated images by preserving high-level image features. The optimization process involves minimizing Euclidean distances between synthesized and target image feature maps, which are derived from the pre-trained VGG19 network. Experimental findings attest to the effectiveness of this method, revealing noteworthy improvements of 8% in Inception Score, 2% in NIQE Score, and 13% in BRISQUE Score.
Stereo Imaging technology integration into medical diagnostics and surgeries brings a great revolution in the field of medical sciences. Now, surgeons and physicians have better insight into the anatomy of patients...
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Stereo Imaging technology integration into medical diagnostics and surgeries brings a great revolution in the field of medical sciences. Now, surgeons and physicians have better insight into the anatomy of patients...
Stereo Imaging technology integration into medical diagnostics and surgeries brings a great revolution in the field of medical sciences. Now, surgeons and physicians have better insight into the anatomy of patients' organs. Like other technologies, stereo cameras have limitations, e.g., low resolution (LR) and blurry output images. Currently, most of the proposed techniques for super-resolution focus on developing complex blocks and complicated loss functions, which cause high system complexity. We proposed a combined channel and spatial attention block to extract features incorporated with a specific but very strong parallax attention module (PAM) for endoscopic image super-resolution. The proposed model is trained using the da Vinci dataset on scales 2 and 4. Our proposed model has improved PSNR up to 2.12 dB for scale 2 and 1.29 dB for scale 4, while SSIM is improved by 0.03 for scale 2 and 0.0008 for scale 4. By incorporating this method, diagnosis and treatment for endoscopic images can be more accurate and effective.
In recent years, convolutional neural networks have significantly advanced image segmentation, particularly for brain images, where important edge features are automatically found. However, accurate segmentation of tu...
ISBN:
(数字)9798350366860
ISBN:
(纸本)9798350366877
In recent years, convolutional neural networks have significantly advanced image segmentation, particularly for brain images, where important edge features are automatically found. However, accurate segmentation of tumors in a brain remains a challenge across different magnetic resonance modalities, like T1, T2, T1ce, and FLAIR. Using a simple gradient map as an input to the neural networks is not effective due to variations in cross-modality image characteristics. To address this issue, we introduced multi-scale gradient maps that incorporate Holistically Nested Edge Detection (HED) and dilated convolutions into the UNet model. The HED model captures detailed gradient information, enhancing structural feature identification across modalities, while dilated convolutions expand the UNet receptive field for better contextual understanding without increasing parameters. Our method was trained and evaluated on the BraTS2018 dataset. The experimental results demonstrate significant improvements in segmentation accuracy and robustness. Specifically, our method achieved a Dice Similarity Coefficient (DSC) of 0.6902 for T2 to T1ce, 0.6858 for T2 to T1, 0.4329 for FLAIR to T1, and 0.6004 for FLAIR to T1ce, outperforming previous state-of-the-art methods. This demonstrates the effectiveness of our approach in enhancing segmentation performance across different MR image modalities.
The Diameter at Breast Height (DBH) measurements are essential for forest management and carbon absorption estimation in environmental challenges. Traditional DBH measurements require more precision and efficiency, es...
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ISBN:
(数字)9798350383027
ISBN:
(纸本)9798350383034
The Diameter at Breast Height (DBH) measurements are essential for forest management and carbon absorption estimation in environmental challenges. Traditional DBH measurements require more precision and efficiency, especially in inaccessible forests. Existing methods, including manual measurement or remote sensing, struggle with precision in low-intensity point cloud areas. Our technique solves these limitations by analyzing the entire tree stem by the process, including convex hull construction and advanced noise reduction using real-world datasets. It has reduced the Root Mean Squared Error (RMSE) to 0.020, or 92.25% improvement, and the bias has decreased to -0.008. These enhancements highlight the accuracy and reliability of our DBH estimation technique.
Recent research on single image super-resolution (SISR) using deep convolutional neural networks (CNNs) has shown significant development in the area computer vision-based tasks specially image and video processing. S...
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