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It is a simple task for humans to visually identify objects. However, computer-based image recognition remains challenging. In this paper we describe an approach for image recognition with specific focus on automated recognition of plants and flowers. The approach taken utilizes deep learning capabilities and unlike other approaches that focus on static images for feature classification, we utilize...
A new learning model for image resampling with convolutional neural network is proposed. Its main idea is the dataset preparation method for deep learning. The proposed algorithm can work with noisy and noiseless images and provides good quality for wide noise level range. The method was tested using standard datasets and was also applied for retinal image resampling.
In this paper, we propose an image super-resolution (SR) method using multi-channel-input convolutional neural networks (MC-SRCNN) where the multi-channel input is comprised of an original low-resolution (LR) input and its edge-enhanced and variously interpolated version. Recently, Super-Resolution Convolutional Neural Network (SRCNN) showed remarkable performance. However, in SRCNN, deep layer structures...
This paper proposes image super-resolution techniques with multi-channel convolutional neural networks (CNN). In the proposed method, output pixels are classified into four groups depending on their positions. Those groups are generated from separate channels of the CNN. Finally, they are synthesized into a 2−2 magnified image. This architecture can enlarge images directly without bicubic interpolation...
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