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computer-vision

Image Rescaling

3260 papers • 126 benchmarks • 313 datasets

Image rescaling is a bidirectional operation, which first downscales high-resolution images to fit various display screens or to be storage- and bandwidth-friendly, and afterward upscales the corresponding low-resolution images to recover the original resolution or the details in the zoom-in images.

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in image-rescaling

Trend

Dataset

Best Model

Actions

DIV2K val-q30-4x

DIV2K val-q30-4x

DIV2K val-q50-4x

DIV2K val-q50-4x

DIV2K val-q70-4x

Libraries

i

Use these libraries to find image-rescaling models and implementations

Datasets

DIV2K

Set5

Subtasks

No subtasks available.

Most implemented papers

Invertible Image Rescaling

Zhouchen Lin, Jiang Bian, Guolin Ke, Tie-Yan Liu, Di He, Mingqing Xiao, Shuxin Zheng, Chang Liu, Yaolong Wang•Mon May 11 2020

An Invertible Rescaling Net (IRN) is developed with deliberately designed framework and objectives to produce visually-pleasing low-resolution images and meanwhile capture the distribution of the lost information using a latent variable following a specified distribution in the downscaling process.

274

Content

Introduction Benchmarks Datasets Subtasks Libraries Papers

DIV2K val-q70-4x

DIV2K val-q90-4x

DIV2K val-q90-4x

DIV2K val-q30-2x

DIV2K val-q30-2x

DIV2K val-q50-2x

DIV2K val-q50-2x

DIV2K val-q70-2x

DIV2K val-q70-2x

DIV2K val-q90-2x

DIV2K val-q90-2x

Set5-2x

Set5-2x

Set14-2x

Set14-2x

BSD100-2x

BSD100-2x

Urban100-2x

Urban100-2x

DIV2K val-2x

DIV2K val-2x

Set5-4x

Set5-4x

Set14-4x

Set14-4x

BSD100-4x

BSD100-4x

Urban100-4x

Urban100-4x

DIV2K val-4x

DIV2K val-4x

0

Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescaling

K. Zhang, L. Gool, Martin Danelljan, Radu Timofte, Jingyun Liang, Andreas Lugmayr•Tue Aug 10 2021

The hierarchical conditional flow (HCFlow) is proposed as a unified framework for image SR and image rescaling and achieves state-of-the-art performance in terms of both quantitative metrics and visual quality.

121 0

Faithful Extreme Rescaling via Generative Prior Reciprocated Invertible Representations

Shengfeng He, Jianxiong Pan, Zhixuan Zhong, Liangyu Chai, Yang Zhou, Bailin Deng•Tue May 31 2022

This paper presents a Generative prior ReciprocAted Invertible rescaling Network (GRAIN) for generating faithful high-resolution (HR) images from low-resolution (LR) invertible images with an extreme upscaling factor (64×). Previous researches have leveraged the prior knowledge of a pretrained GAN model to generate high-quality upscaling results. However, they fail to produce pixel-accurate results due to the highly ambiguous extreme mapping process. We remedy this problem by introducing a reciprocated invertible image rescaling process, in which high-resolution information can be delicately embedded into an invertible low-resolution image and generative prior for a faithful HR reconstruction. In particular, the invertible LR features not only carry significant HR semantics, but also are trained to predict scale-specific latent codes, yielding a preferable utilization of generative features. On the other hand, the enhanced generative prior is re-injected to the rescaling process, compensating the lost details of the invertible rescaling. Our reciprocal mechanism perfectly integrates the advantages of invertible encoding and generative prior, leading to the first feasible extreme rescaling solution. Extensive experiments demonstrate superior performance against state-of-the-art upscaling methods. Code is available at https://github.com/cszzx/GRAIN.

12 0

Enhancing Image Rescaling using Dual Latent Variables in Invertible Neural Network

Xiaoxia Zhou, Min Zhang, Zhihong Pan, C. J. Kuo•Sat Jul 23 2022

A new downscaling latent variable, in addition to the original one representing uncertainties in image upscaling, is introduced to model variations in the image down Scaling process, which can improve image up Scaling accuracy consistently without sacrificing image quality in downscaled LR images.

10 0

Invertible Rescaling Network and Its Extensions

Zhouchen Lin, Mingqing Xiao, Shuxin Zheng, Chang Liu, Tie-Yan Liu•Sat Oct 08 2022

A novel invertible framework is proposed to handle the bidirectional degradation and restoration from a new perspective, i.e. invertable bijective transformation, to model the information loss of pre-degradation in the form of distribution, which could mitigate the ill-posed problem during post-restoration.

37 0

Thermal Chameleon: Task-Adaptive Tone-Mapping for Radiometric Thermal-Infrared Images

Younggun Cho, Ayoung Kim, Jeong-Man Kim, Dong-Guw Lee•Wed Oct 23 2024

Given the same image, TCNet tone-maps different representations of TIR images tailored for each specific task, eliminating the heuristic image rescaling preprocessing and reliance on the extensive prior knowledge of the scene temperature or task-specific characteristics.

1 0

Real-time 6K Image Rescaling with Rate-distortion Optimization

Chenyang Qi•Sun Apr 02 2023

A novel frame-work (HyperThumbnail) for real-time 6K rate-distortion-aware image rescaling that minimizes the file size of the embedding LR JPEG thumbnail while maximizing HR reconstruction quality and reconstructs a high-fidelity HR image from the LR one in real time.

12 0

Plug-and-Play Tri-Branch Invertible Block for Image Rescaling

Jingwei Bao, Jinhua Hao, Pengcheng Xu, Ming Sun, Chao Zhou, Shuyuan Zhu•Tue Dec 17 2024

A plug-and-play tri-branch invertible block (T-InvBlocks) that decomposes the low-frequency branch into luminance (Y) and chrominance (CbCr) components, reducing redundancy and enhancing feature processing.

4 0

DINN360: Deformable Invertible Neural Network for Latitude-aware 360° Image Rescaling

Yunjin Chen, Lai Jiang, L. Sigal, Yichen Guo, Mai Xu•Wed May 31 2023

With the rapid development of virtual reality, 360° images have gained increasing popularity. Their wide field of view necessitates high resolution to ensure image quality. This, however, makes it harder to acquire, store and even process such 360° images. To alleviate this issue, we propose the first attempt at 360° image rescaling, which refers to downscaling a 360° image to a visually valid lowresolution (LR) counterpart and then upscaling to a highresolution (HR) 360° image given the LR variant. Specifically, we first analyze two 360° image datasets and observe several findings that characterize how 360° images typically change along their latitudes. Inspired by these findings, we propose a novel deformable invertible neural network (INN), named DINN360, for latitude-aware 360° image rescaling. In DINN360, a deformable INN is designed to downscale the LR image, and project the high-frequency (HF) component to the latent space by adaptively handling various deformations occurring at different latitude regions. Given the downscaled LR image, the high-quality HR image is then reconstructed in a conditional latitude-aware manner by recovering the structure-related HF component from the latent space. Extensive experiments over four public datasets show that our DINN360 method performs considerably better than other state-of-the-art methods for 2 x, 4 x and 8 x 360° image rescaling.

9 0

Self-Asymmetric Invertible Network for Compression-Aware Image Rescaling

Jinhai Yang, Mengxi Guo, Shijie Zhao, Junlin Li, Li Zhang•Fri Mar 03 2023

The Self-Asymmetric Invertible Network (SAIN) is proposed, an end-to-end asymmetric framework with two separate bijective mappings for high-quality and compressed LR images, respectively, and a set of losses to regularize the learned LR images and enhance the invertibility.

9 0

Adding a benchmark result helps the community track progress.

Image Rescaling | State-of-the-Art