Deep learning-based skin lesion analysis using hybrid ResUNet++ and modified AlexNet-Random Forest for enhanced segmentation and classification. [PDF]
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Towards reliable object representation via sparse directional patches and spatial center cues. [PDF]
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BCCHI-HCNN: Breast Cancer Classification from Histopathological Images Using Hybrid Deep CNN Models. [PDF]
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Nonseparable Shearlet Transform
IEEE Transactions on Image Processing, 2013Over the past few years, various representation systems which sparsely approximate functions governed by anisotropic features, such as edges in images, have been proposed. Alongside the theoretical development of these systems, algorithmic realizations of the associated transforms are provided.
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A Shearlet Approach to Edge Analysis and Detection [PDF]
IEEE Transactions on Image Processing, 2009 It is well known that the wavelet transform provides a very effective framework for analysis of multiscale edges. In this paper, we propose a novel approach based on the shearlet transform: a multiscale directional transform with a greater ability to localize distributed discontinuities such as edges.
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Deep Shearlet Residual Learning Network for Single Image Super-Resolution
IEEE Transactions on Image Processing, 2021Recently, the residual learning strategy has been integrated into the convolutional neural network (CNN) for single image super-resolution (SISR), where the CNN is trained to estimate the residual images.
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A Reverberation Noise Suppression Method of Sonar Image Based on Shearlet Transform
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The registration of synthetic aperture radar (SAR) and optical images is still a challenging task due to the influence of the potential nonlinear intensity differences and severe speckle noise.
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