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deconvolution kernel

时间: 2023-11-11 15:02:41 浏览: 85

Blind Deconvolution With Nonlocal Similarity and l(0) Sparsity for Noisy Image

The blind image deconvolution techniques with sparsity prior in gradient domain are sensitive to noise, even a small amount of noise. To address this roblem, in this letter, we propose a novel blind deconvolution model that combines lowrank property, nonlocal similarity, and l0 sparsity prior. Low-rank property akes the proposed deblurring model robust to image noise. The joint utilization of nonlocal similarity and l0 sparsity prior has improved the accuracy of blur kernel estimation and restor

Deconvolution kernel, also known as transposed convolution kernel, is a learnable filter used in deconvolutional neural networks. It is the inverse operation of convolution and is used to upsample feature maps in order to reconstruct an image or to perform semantic segmentation. The deconvolution kernel is a matrix of weights that is typically initialized randomly and learned during the training process. The size of the kernel depends on the desired output size and the stride used during the convolutional operation. In contrast to convolutional kernels, which perform a sliding dot product between the kernel and the input feature map, deconvolution kernels perform a sliding dot product between the kernel and the output feature map. This results in an upsampled feature map that can be used for image generation or segmentation.

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