Towards Unified Deep Image Deraining: A Survey and A New Benchmark

• URL: http://arxiv.org/abs/2310.03535v1
• Date: Thu, 5 Oct 2023 13:35:00 GMT
• Title: Towards Unified Deep Image Deraining: A Survey and A New Benchmark
• Authors: Xiang Chen, Jinshan Pan, Jiangxin Dong, Jinhui Tang
• Abstract summary: We provide a comprehensive review of existing image deraining method and provide a unify evaluation setting to evaluate the performance of image deraining methods. We construct a new high-quality benchmark named HQ-RAIN to further conduct extensive evaluation, consisting of 5,000 paired high-resolution synthetic images with higher harmony and realism.
• Score: 72.53380760079396
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent years have witnessed significant advances in image deraining due to the kinds of effective image priors and deep learning models. As each deraining approach has individual settings (e.g., training and test datasets, evaluation criteria), how to fairly evaluate existing approaches comprehensively is not a trivial task. Although existing surveys aim to review of image deraining approaches comprehensively, few of them focus on providing unify evaluation settings to examine the deraining capability and practicality evaluation. In this paper, we provide a comprehensive review of existing image deraining method and provide a unify evaluation setting to evaluate the performance of image deraining methods. We construct a new high-quality benchmark named HQ-RAIN to further conduct extensive evaluation, consisting of 5,000 paired high-resolution synthetic images with higher harmony and realism. We also discuss the existing challenges and highlight several future research opportunities worth exploring. To facilitate the reproduction and tracking of the latest deraining technologies for general users, we build an online platform to provide the off-the-shelf toolkit, involving the large-scale performance evaluation. This online platform and the proposed new benchmark are publicly available and will be regularly updated at http://www.deraining.tech/.

Related papers

• Confidence-aware Reward Optimization for Fine-tuning Text-to-Image Models [85.96013373385057]
  Fine-tuning text-to-image models with reward functions trained on human feedback data has proven effective for aligning model behavior with human intent. However, excessive optimization with such reward models, which serve as mere proxy objectives, can compromise the performance of fine-tuned models. We propose TextNorm, a method that enhances alignment based on a measure of reward model confidence estimated across a set of semantically contrastive text prompts.
  arXiv  Detail & Related papers  (2024-04-02T11:40:38Z)
• QUASAR: QUality and Aesthetics Scoring with Advanced Representations [20.194917729936357]
  This paper introduces a new data-driven, non-parametric method for image quality and aesthetics assessment. We eliminate the need for expressive textual embeddings by proposing efficient image anchors in the data.
  arXiv  Detail & Related papers  (2024-03-11T16:21:50Z)
• Adversarial Purification and Fine-tuning for Robust UDC Image Restoration [25.02234305857731]
  Under-Display Camera (UDC) technology faces unique image degradation challenges exacerbated by the susceptibility to adversarial perturbations. This study focuses on the enhancement of Under-Display Camera (UDC) image restoration models, focusing on their robustness against adversarial attacks.
  arXiv  Detail & Related papers  (2024-02-21T09:06:04Z)
• WAVES: Benchmarking the Robustness of Image Watermarks [67.955140223443]
  WAVES (Watermark Analysis Via Enhanced Stress-testing) is a benchmark for assessing image watermark robustness. We integrate detection and identification tasks and establish a standardized evaluation protocol comprised of a diverse range of stress tests. We envision WAVES as a toolkit for the future development of robust watermarks.
  arXiv  Detail & Related papers  (2024-01-16T18:58:36Z)
• CarPatch: A Synthetic Benchmark for Radiance Field Evaluation on Vehicle Components [77.33782775860028]
  We introduce CarPatch, a novel synthetic benchmark of vehicles. In addition to a set of images annotated with their intrinsic and extrinsic camera parameters, the corresponding depth maps and semantic segmentation masks have been generated for each view. Global and part-based metrics have been defined and used to evaluate, compare, and better characterize some state-of-the-art techniques.
  arXiv  Detail & Related papers  (2023-07-24T11:59:07Z)
• Contrastive Learning Based Recursive Dynamic Multi-Scale Network for Image Deraining [47.764883957379745]
  Rain streaks significantly decrease the visibility of captured images. Existing deep learning-based image deraining methods employ manually crafted networks and learn a straightforward projection from rainy images to clear images. We propose a contrastive learning-based image deraining method that investigates the correlation between rainy and clear images.
  arXiv  Detail & Related papers  (2023-05-29T13:51:41Z)
• A survey on IQA [0.0]
  This article will review the concepts and metrics of image quality assessment and also video quality assessment. It briefly introduce some methods of full-reference and semi-reference image quality assessment, and focus on the non-reference image quality assessment methods based on deep learning.
  arXiv  Detail & Related papers  (2021-08-29T10:52:27Z)
• Lighting the Darkness in the Deep Learning Era [118.35081853500411]
  Low-light image enhancement (LLIE) aims at improving the perception or interpretability of an image captured in an environment with poor illumination. Recent advances in this area are dominated by deep learning-based solutions. We provide a comprehensive survey to cover various aspects ranging from algorithm taxonomy to unsolved open issues.
  arXiv  Detail & Related papers  (2021-04-21T19:12:19Z)
• Unifying Remote Sensing Image Retrieval and Classification with Robust Fine-tuning [3.6526118822907594]
  We aim at unifying remote sensing image retrieval and classification with a new large-scale training and testing dataset, SF300. We show that our framework systematically achieves a boost of retrieval and classification performance on nine different datasets compared to an ImageNet pretrained baseline.
  arXiv  Detail & Related papers  (2021-02-26T11:01:30Z)
• Regression or Classification? New Methods to Evaluate No-Reference Picture and Video Quality Models [45.974399400141685]
  We propose two new methods to evaluate and compare no-reference quality models at coarser levels. We conduct a benchmark experiment of popular no-reference quality models on recent in-the-wild picture and video quality datasets.
  arXiv  Detail & Related papers  (2021-01-30T05:40:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.