We present a Deep Convolutional Neural Network architecture which serves as a generic image-to-image regressor that can be trained end-to-end without any further machinery. Our proposed architecture: the Recursively Branched Deconvolutional Network (RBDN) develops a cheap multi-context image representation very early on using an efficient recursive branching scheme with extensive parameter sharing and learnable upsampling. This multi-context representation is subjected to a highly non-linear locality preserving transformation by the remainder of our network comprising of a series of convolutions/deconvolutions without any spatial downsampling. The RBDN architecture is fully convolutional and can handle variable sized images during inference. We provide qualitative/quantitative results on $3$ diverse tasks: relighting, denoising and colorization and show that our proposed RBDN architecture obtains comparable results to the state-of-the-art on each of these tasks when used off-the-shelf without any post processing or task-specific architectural modifications.

本文提出了一种深度卷积神经网络体系结构，用作通用的图像到图像回归器，可以直接进行端到端的训练。我们提出的体系结构——递归分支反卷积网络（RBDN）采用高效的递归分支方案和可学习的上采样，早期开发了一种廉价的多重上下文图像表示方法。该RBDN体系结构是完全卷积的，在推断过程中可以处理不同大小的图像。我们在relighting、去噪和着色3个不同的任务上提供了定性/定量结果，并表明，当不进行任何后处理或任务特定的架构修改时，我们提出的RBDN体系结构在每个任务上都可以获得与最先进技术相当的结果。

通用深度图像到图像回归