Deformable Kernel Networks for Joint Image Filtering

Joint Image Filtering with Deep Convolutional Networks

Joint image filters leverage the guidance image as a prior and transfer the structural details from the guidance image to the target image for suppressing noise or enhancing spatial resolution. Existing methods either rely on various explicit filter constructions or hand-designed objective functions, thereby making it difficult to understand, improve, and accelerate these filters in a coherent ...

Deep Joint Image Filtering

Joint image filters can leverage the guidance image as a prior and transfer the structural details from the guidance image to the target image for suppressing noise or enhancing spatial resolution. Existing methods rely on various kinds of explicit filter construction or handdesigned objective functions. It is thus difficult to understand, improve, and accelerate them in a coherent framework. I...

Joint Kernel Learning for Supervised Image Segmentation

This paper considers a supervised image segmentation algorithm based on joint-kernelized structured prediction. In the proposed algorithm, correlation clustering over a superpixel graph is conducted using a non-linear discriminant function, where the parameters are learned by a kernelized-structured support vector machine (SSVM). For an input superpixel image, correlation clustering is used to ...

Deep Joint Image Filtering Supplementary Material

Efficient shift-variant image restoration using deformable filtering (Part I)

In this study, we propose using the least squares optimal deformable filtering approximation as an efficient tool for linear shift variant (SV) filtering, in the context of restoring SV-degraded images. Based on this technique we propose a new formalism for linear SV operators, from which an efficient way to implement the transposed SVfiltering is derived. We also provide a method for implement...