# Generalized Deep Image to Image Regression

arXiv: https://arxiv.org/abs/1612.03268 · HTML: https://mlsanity.com/paper/1612.03268/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2016.

## Built on by (most cited first, 14 of 14)

- [Learning Operators with Coupled Attention](https://mlsanity.com/paper/2201.01032/) (2022)
- [Principal Uncertainty Quantification with Spatial Correlation for Image Restoration Problems](https://mlsanity.com/paper/2305.10124/) (2023)
- [The Swiss Army Knife for Image-to-Image Translation: Multi-Task Diffusion Models](https://mlsanity.com/paper/2204.02641/) (2022)
- [Variational Autoencoding Neural Operators](https://mlsanity.com/paper/2302.10351/) (2023)
- [Dense residual Transformer for image denoising](https://mlsanity.com/paper/2205.06944/) (2022)
- [Learning Task-Oriented Flows to Mutually Guide Feature Alignment in Synthesized and Real Video Denoising](https://mlsanity.com/paper/2208.11803/) (2022)
- [Online Infinite-Dimensional Regression: Learning Linear Operators](https://mlsanity.com/paper/2309.06548/) (2023)
- [Thunder: Thumbnail based Fast Lightweight Image Denoising Network](https://mlsanity.com/paper/2205.11823/) (2022)
- [Blind-Spot Guided Diffusion for Self-supervised Real-World Denoising](https://mlsanity.com/paper/2509.16091/) (2025)
- [Towards Safer Transportation: a self-supervised learning approach for traffic video deraining](https://mlsanity.com/paper/2110.07379/) (2021)
- [Gated Recurrent Unit for Video Denoising](https://mlsanity.com/paper/2210.09135/) (2022)
- [Integrative Variational Autoencoders for Generative Modeling of an Image Outcome with Multiple Input Images](https://mlsanity.com/paper/2402.02734/) (2024)
- [A Practical Gated Recurrent Transformer Network Incorporating Multiple Fusions for Video Denoising](https://mlsanity.com/paper/2409.06603/) (2024)
- [Computer-aided Colorization State-of-the-science: A Survey](https://mlsanity.com/paper/2410.02288/) (2024)

## Abstract (arXiv)

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.