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KERNEL PREDICTION WITH KERNEL DICTIONARY IN IMAGE DENOISING

机译:基于核字典的核预测在图像去噪中的应用

摘要

Certain embodiments involve techniques for efficiently estimating denoising kernels for generating denoised images. For instance, a neural network receives a noisy reference image to denoise. The neural network uses a kernel dictionary of base kernels and generates a coefficient vector for each pixel in the reference image such that the coefficient vector includes a coefficient value for each base kernel in the kernel dictionary, where the base kernels are combined to generate a denoising kernel and each coefficient value indicates a contribution of a given base kernel to a denoising kernel. The neural network calculates the denoising kernel for a given pixel by applying the coefficient vector for that pixel to the kernel dictionary. The neural network applies each denoising kernel to the respective pixel to generate a denoised output image.
机译:某些实施例涉及用于有效估计去噪核以生成去噪图像的技术。例如,神经网络接收噪声参考图像以进行去噪。神经网络使用基本核的核字典,并为参考图像中的每个像素生成系数向量,使得系数向量包括核字典中每个基本核的系数值,其中,基础核被组合以生成去噪核,每个系数值表示给定基础核对去噪核的贡献。神经网络通过将给定像素的系数向量应用于核字典来计算该像素的去噪核。神经网络将每个去噪核应用于各个像素,以生成去噪输出图像。

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