Convolutional Networks with MuxOut Layers as Multi-rate Systems for Image Upscaling

We interpret convolutional networks as adaptive filters and combine them with so-called MuxOut layers to efficiently upscale low resolution images. We formalize this interpretation by deriving a linear and space-variant structure of a convolutional network when its activations are fixed. We introduce general purpose algorithms to analyze a network and show its overall filter effect for each given location. We use this analysis to evaluate two types of image upscalers: deterministic upscalers that target the recovery of details from original content; and second, a new generation of upscalers that can sample the distribution of upscale aliases (images that share the same downscale version) that look like real content.

READ FULL TEXT

page 7

page 8

research
07/10/2019

Neural Networks as Explicit Word-Based Rules

Filters of convolutional networks used in computer vision are often visu...
research
11/30/2017

Convolutional Networks with Adaptive Computation Graphs

Do convolutional networks really need a fixed feed-forward structure? Of...
research
10/07/2018

Image Completion on CIFAR-10

This project performed image completion on CIFAR-10, a dataset of 60,000...
research
11/30/2020

Why Convolutional Networks Learn Oriented Bandpass Filters: Theory and Empirical Support

It has been repeatedly observed that convolutional architectures when ap...
research
08/14/2019

A Tour of Convolutional Networks Guided by Linear Interpreters

Convolutional networks are large linear systems divided into layers and ...
research
07/13/2020

A new approach to descriptors generation for image retrieval by analyzing activations of deep neural network layers

In this paper, we consider the problem of descriptors construction for t...
research
10/11/2017

Joint Image Filtering with Deep Convolutional Networks

Joint image filters leverage the guidance image as a prior and transfer ...

Please sign up or login with your details

Forgot password? Click here to reset