Deconvolution layers apply mathematical operations to reverse the effect of convolution layers. First, let's get a basic understanding of what a convolution layer does.
A convolution layer is one of the building blocks of CNN. An image is provided to the convolution layer, which processes it according to the given parameters. These parameters include the filters, which are learned through the training data. The convolution layer's purpose is to reduce the size of an image while ensuring that no information about the image is lost. The output of the convolution layer is the reduced image or a matrix called the feature map.
A deconvolution layer does the exact opposite of the convolution layer. The output of the convolution layer can be given as input to the deconvolution layer and will provide us with the original input image as its output. Deconvolution is also known as transposed convolution. So if a convolution compresses the information provided in different pixels in one pixel, the deconvolution layer takes that one pixel and gives us a spread of pixels.
Deconvolution has various applications such as image, digital signal, GAN, and seismic processing. An example of deconvolution in image processing would be image segmentation. Image segmentation using deconvolution makes it much easier to analyze images in most cases. Deconvolution is used whenever a need arises to reconstruct an image.
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