# NSSR-DIL: Null-Shot Image Super-Resolution Using Deep Identity Learning

arXiv: https://arxiv.org/abs/2409.12165 · HTML: https://mlsanity.com/paper/2409.12165/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 30 of its 48 references that are in the map.
- First posted on arXiv 17 Sep 2024.

## Builds on (most cited first, 30 of 30)

- [Density estimation using Real NVP](https://mlsanity.com/paper/1605.08803/) (2016)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [Restormer: Efficient Transformer for High-Resolution Image Restoration](https://mlsanity.com/paper/2111.09881/) (2021)
- [Enhanced Deep Residual Networks for Single Image Super-Resolution](https://mlsanity.com/paper/1707.02921/) (2017)
- [Image Super-Resolution Using Very Deep Residual Channel Attention Networks](https://mlsanity.com/paper/1807.02758/) (2018)
- [ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks](https://mlsanity.com/paper/1809.00219/) (2018)
- [Image Super-Resolution Using Deep Convolutional Networks](https://mlsanity.com/paper/1501.00092/) (2014)
- [NICE: Non-linear Independent Components Estimation](https://mlsanity.com/paper/1410.8516/) (2014)
- [Exact solutions to the nonlinear dynamics of learning in deep linear neural networks](https://mlsanity.com/paper/1312.6120/) (2013)
- [Designing a Practical Degradation Model for Deep Blind Image Super-Resolution](https://mlsanity.com/paper/2103.14006/) (2021)
- [Toward Real-World Single Image Super-Resolution: A New Benchmark and A New Model](https://mlsanity.com/paper/1904.00523/) (2019)
- [NIMA: Neural Image Assessment](https://mlsanity.com/paper/1709.05424/) (2017)
- ["Zero-Shot" Super-Resolution using Deep Internal Learning](https://mlsanity.com/paper/1712.06087/) (2017)
- [On the Optimization of Deep Networks: Implicit Acceleration by Overparameterization](https://mlsanity.com/paper/1802.06509/) (2018)
- [The Loss Surfaces of Multilayer Networks](https://mlsanity.com/paper/1412.0233/) (2014)
- [Component Divide-and-Conquer for Real-World Image Super-Resolution](https://mlsanity.com/paper/2008.01928/) (2020)
- [Deep Learning without Poor Local Minima](https://mlsanity.com/paper/1605.07110/) (2016)
- [Learning a Single Convolutional Super-Resolution Network for Multiple Degradations](https://mlsanity.com/paper/1712.06116/) (2017)
- [Blind Super-Resolution Kernel Estimation using an Internal-GAN](https://mlsanity.com/paper/1909.06581/) (2019)
- [Fast and Accurate Image Super-Resolution with Deep Laplacian Pyramid Networks](https://mlsanity.com/paper/1710.01992/) (2017)
- [Unsupervised Image Super-Resolution using Cycle-in-Cycle Generative Adversarial Networks](https://mlsanity.com/paper/1809.00437/) (2018)
- [Dual Aggregation Transformer for Image Super-Resolution](https://mlsanity.com/paper/2308.03364/) (2023)
- [Zoom To Learn, Learn To Zoom](https://mlsanity.com/paper/1905.05169/) (2019)
- [Frequency Separation for Real-World Super-Resolution](https://mlsanity.com/paper/1911.07850/) (2019)
- [Flow-based Kernel Prior with Application to Blind Super-Resolution](https://mlsanity.com/paper/2103.15977/) (2021)
- [Unified Dynamic Convolutional Network for Super-Resolution with Variational Degradations](https://mlsanity.com/paper/2004.06965/) (2020)
- [Camera Lens Super-Resolution](https://mlsanity.com/paper/1904.03378/) (2019)
- [ImagePairs: Realistic Super Resolution Dataset via Beam Splitter Camera Rig](https://mlsanity.com/paper/2004.08513/) (2020)
- [RZSR: Reference-based Zero-Shot Super-Resolution with Depth Guided Self-Exemplars](https://mlsanity.com/paper/2208.11313/) (2022)
- [Lightweight Feature Fusion Network for Single Image Super-Resolution](https://mlsanity.com/paper/1902.05694/) (2019)

## Abstract (arXiv)

The present State-of-the-Art (SotA) Image Super-Resolution (ISR) methods employ Deep Learning (DL) techniques using a large amount of image data. The primary limitation to extending the existing SotA ISR works for real-world instances is their computational and time complexities. In this paper, contrary to the existing methods, we present a novel and computationally efficient ISR algorithm that is independent of the image dataset to learn the ISR task. The proposed algorithm reformulates the ISR task from generating the Super-Resolved (SR) images to computing the inverse of the kernels that span the degradation space. We introduce Deep Identity Learning, exploiting the identity relation between the degradation and inverse degradation models. The proposed approach neither relies on the ISR dataset nor on a single input low-resolution (LR) image (like the self-supervised method i.e. ZSSR) to model the ISR task. Hence we term our model as Null-Shot Super-Resolution Using Deep Identity Learning (NSSR-DIL). The proposed NSSR-DIL model requires fewer computational resources, at least by an order of 10, and demonstrates a competitive performance on benchmark ISR datasets. Another salient aspect of our proposition is that the NSSR-DIL framework detours retraining the model and remains the same for varying scale factors like X2, X3, and X4. This makes our highly efficient ISR model more suitable for real-world applications.