image resizing plays a very important role in imageprocessing, offering good scalability and extensibility. Currently, there are some issues with existing scaling interpolation algorithms, such as complex hardware im...
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The field of imageprocessing widely utilizes scene text segmentation technology, with applications extending to image editing and font style transfer. These applications enhance image understanding quality and aid in...
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Text to image synthesis is the translation of images from the input language text. The learning process can become easier when the spoken words can visualize with the images. It is one of the popular research field in...
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With the value of digital data increasing in recent years, substantial improvements have been made in imageprocessing. It is now simpler to produce large image datasets. picture processing is carrying out a variety o...
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Single-photon camera is a novel camera type that utilizes image sensor with photon-counting capability. Recently, the potential of such sensors to achieve high spatial resolutions (e.g., 10/chip) and frame rates (e.g....
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Single image Super-Resolution (SISR) based on deep learning methods has been widely studied for applications on remote sensing images. With limited remote sensing images, most of the existing SISR methods simply adopt...
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ISBN:
(纸本)9781728198354
Single image Super-Resolution (SISR) based on deep learning methods has been widely studied for applications on remote sensing images. With limited remote sensing images, most of the existing SISR methods simply adopt the regular data augmentation approaches (such as flip) in natural images to improve model performance. Considering the fact that remote sensing images are all taken from a bird's-eye view and objects appear in multiple directions, we first introduce rotation augmentation method in remote sensing images to promote diversity of samples dramatically, as rotation does not cause semantic problems like people standing upside down in natural images. However, image rotation at various angles implemented by interpolation will cause the inconsistent pixel distribution problem for the pixel level task. Thus, we propose Transformation Consistency Loss Function (TCLF) to narrow the gap between the augmented and original distribution, while expanding the feature space with rotation augmentation method. Extensive experiments are performed on UC-Merced Land-use dataset of 21 remote sensing scenes, and the results as well as ablation studies demonstrate our proposed method outperforms mainstream methods.
Multimodal medical image fusion is vital for extracting complementary information and generating comprehensive images in clinical applications. However, existing deep learning-based fusion approaches face challenges i...
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In this work, we implemented the discrete Fourier transform (DFT) using a Pt/Al2O3/AlOx/W resistive random-access memory (ReRAM) for high-precision signal processing. By introducing the bit-slicing method, the ReRAM d...
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Adaptive image restoration models can restore images with different degradation levels at inference time without the need to retrain the model. We present an approach that is highly accurate and allows a significant r...
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BP neural network has been applied extensively in imageprocessing, with ID card recognition systems being utilized in every facet of everyday life. To reduce the error rate and enhance the effectiveness of use, we em...
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