19 research outputs found
Multigrid Backprojection Super-Resolution and Deep Filter Visualization
We introduce a novel deep-learning architecture for image upscaling by large
factors (e.g. 4x, 8x) based on examples of pristine high-resolution images. Our
target is to reconstruct high-resolution images from their downscale versions.
The proposed system performs a multi-level progressive upscaling, starting from
small factors (2x) and updating for higher factors (4x and 8x). The system is
recursive as it repeats the same procedure at each level. It is also residual
since we use the network to update the outputs of a classic upscaler. The
network residuals are improved by Iterative Back-Projections (IBP) computed in
the features of a convolutional network. To work in multiple levels we extend
the standard back-projection algorithm using a recursion analogous to
Multi-Grid algorithms commonly used as solvers of large systems of linear
equations. We finally show how the network can be interpreted as a standard
upsampling-and-filter upscaler with a space-variant filter that adapts to the
geometry. This approach allows us to visualize how the network learns to
upscale. Finally, our system reaches state of the art quality for models with
relatively few number of parameters.Comment: Spotlight paper in the Thirty-Third AAAI Conference on Artificial
Intelligence (AAAI-19
Fourier-Domain Optimization for Image Processing
Image optimization problems encompass many applications such as spectral fusion, deblurring, deconvolution, dehazing, matting, reflection removal and image interpolation, among others. With current image sizes in the order of megabytes, it is extremely expensive to run conventional algorithms such as gradient descent, making them unfavorable especially when closed-form solutions can be derived and computed efficiently. This paper explains in detail the framework for solving convex image optimization and deconvolution in the Fourier domain. We begin by explaining the mathematical background and motivating why the presented setups can be transformed and solved very efficiently in the Fourier domain. We also show how to practically use these solutions, by providing the corresponding implementations. The explanations are aimed at a broad audience with minimal knowledge of convolution and image optimization. The eager reader can jump to Section 3 for a footprint of how to solve and implement a sample optimization function, and Section 5 for the more complex cases