In recent years, it has become popular to tackle image restoration tasks with a single pretrained diffusion model (DM) and data-fidelity guidance, instead of training a dedicated deep neural network per task. However, such "zero-shot" restoration schemes currently require many Neural Function Evaluations (NFEs) for performing well, which may be attributed to the many NFEs needed in the original generative functionality of the DMs. Recently, faster variants of DMs have been explored for image generation. These include Consistency Models (CMs), which can generate samples via a couple of NFEs. However, existing works that use guided CMs for restoration still require tens of NFEs or fine-tuning of the model per task that leads to performance drop if the assumptions during the fine-tuning are not accurate. In this paper, we propose a zero-shot restoration scheme that uses CMs and operates well with as little as 4 NFEs. It is based on a wise combination of several ingredients: better initialization, back-projection guidance, and above all a novel noise injection mechanism. We demonstrate the advantages of our approach for image super-resolution, deblurring and inpainting. Interestingly, we show that the usefulness of our noise injection technique goes beyond CMs: it can also mitigate the performance degradation of existing guided DM methods when reducing their NFE count.
Peking University Shenzhen Graduate School（北京大学深圳研究生院）；Peng Cheng Laboratory（鹏城实验室）
Most existing Image Restoration (IR) models are task-specific, which can not be generalized to different degradation operators. In this work, we propose the Denoising Diffusion Null-Space Model (DDNM), a novel zero-shot framework for arbitrary linear IR problems, including but not limited to image super-resolution, colorization, inpainting, compressed sensing, and deblurring. DDNM only needs a pre-trained off-the-shelf diffusion model as the generative prior, without any extra training or network modifications. By refining only the null-space contents during the reverse diffusion process, we c…
University of Science and Technology of China（中国科学技术大学）
We propose a novel zero-shot multi-frame image restoration method for removing unwanted obstruction elements (such as rains, snow, and moire patterns) that vary in successive frames. It has three stages: transformer pre-training, zero-shot restoration, and hard patch refinement. Using the pre-trained transformers, our model is able to tell the motion difference between the true image information and the obstructing elements. For zero-shot image restoration, we design a novel model, termed SiamTrans, which is constructed by Siamese transformers, encoders, and decoders. Each transformer has a te…
Image restoration is the task of recovering a clean image from a degraded version. In most cases, the degradation is spatially varying, and it requires the restoration network to both localize and restore the affected regions. In this paper, we present a new approach suitable for handling the image-specific and spatially-varying nature of degradation in images affected by practically occurring artifacts such as blur, rain-streaks. We decompose the restoration task into two stages of degradation localization and degraded region-guided restoration, unlike existing methods which directly learn a …
V-Bridge: Bridging Video Generative Priors to Versatile Few-shot Image Restoration
The Hong Kong University of Science and Technology（香港科学与技术大学）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；The Chinese University of Hong Kong（香港中文大学）
DiffBIR: Towards Blind Image Restoration with Generative Diffusion Prior
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Shanghai AI Laboratory（上海人工智能实验室）；The Chinese University of Hong Kong（香港中文大学）
AdaQual-Diff: Diffusion-Based Image Restoration via Adaptive Quality Prompting
Fuzhou University（福州大学）；University of Science and Technology of China（中国科学技术大学）；University of the Chinese Academy of Sciences（中国科学院大学）；Shandong Normal University（山东师范大学）；Sun Yat-sen University（中山大学）
TPGDiff: Hierarchical Triple-Prior Guided Diffusion for Image Restoration
School of Computer Science, Northwestern Polytechnical University, Xi'an, China（西北工业大学计算机学院，西安，中国）；Shenzhen Research Institute of Northwestern Polytechnical University, Shenzhen, China（西北工业大学深圳研究院，深圳，中国）
Yonsei University（延世大学）；Chungnam National University（忠南国立大学）
Regularization-based image restoration has remained an active research topic in computer vision and image processing. It often leverages a guidance signal captured in different fields as an additional cue. In this work, we present a general framework for image restoration, called deeply aggregated alternating minimization (DeepAM). We propose to train deep neural network to advance two of the steps in the conventional AM algorithm: proximal mapping and ?- continuation. Both steps are learned from a large dataset in an end-to-end manner. The proposed framework enables the convolutional neural n…
Tsinghua University（清华大学）；Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Science（中国科学院深圳先进技术研究院）；Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）；University College London（伦敦大学学院）
Most state-of-the-art semantic segmentation approaches only achieve high accuracy in good conditions. In practically-common but less-discussed adverse environmental conditions, their performance can decrease enormously. Existing studies usually cast the handling of segmentation in adverse conditions as a separate post-processing step after signal restoration, making the segmentation performance largely depend on the quality of restoration. In this paper, we propose a novel deep-learning framework to tackle semantic segmentation and image restoration in adverse environmental conditions in a hol…
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；The Chinese University of Hong Kong（香港中文大学）
In image restoration tasks, like denoising and super resolution, continual modulation of restoration levels is of great importance for real-world applications, but has failed most of existing deep learning based image restoration methods. Learning from discrete and fixed restoration levels, deep models cannot be easily generalized to data of continuous and unseen levels. This topic is rarely touched in literature, due to the difficulty of modulating well-trained models with certain hyper-parameters. We make a step forward by proposing a unified CNN framework that consists of few additional par…
Multimodal Prompt Perceiver: Empower Adaptiveness, Generalizability and Fidelity for All-in-One Image Restoration
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；University of Science and Technology of China（中国科学技术大学）
Learning Hierarchical Color Guidance for Depth Map Super-Resolution
Institute of Information Science, Beijing Jiaotong University（北京交通大学信息科学研究所）；School of Control Science and Engineering, Shandong University（山东大学控制科学与工程学院）；Artificial Intelligence Institute, Beijing Normal University（北京师范大学人工智能学院）；College of Electronic Science and Technology, National University of Defense Technology (NUDT)（国防科技大学电子科学与工程学院）；School of Electronics and Communication Engineering, Sun Yat-sen University（中山大学电子与通信工程学院）；School of Computer Science and Technology, Harbin Institute of Technology（哈尔滨工业大学计算机科学与技术学院）；Lingnan University（岭南大学）
Northwestern Polytechnical University（西北工业大学）；The University of Adelaide（阿德莱德大学）
In dynamic scenes, images often suffer from dynamic blur due to superposition of motions or low signal-noise ratio resulted from quick shutter speed when avoiding motions. Recovering sharp and clean results from the captured images heavily depends on the ability of restoration methods and the quality of the input. Although existing research on image restoration focuses on developing models for obtaining better restored results, fewer have studied to evaluate how and which input image leads to superior restored quality. In this paper, to better study an image's potential value that can be explo…
BIR-Adapter: A parameter-efficient diffusion adapter for blind image restoration
Chair of Media Technology, Munich Institute of Robotics and Machine Intelligence（媒体技术教授会，慕尼黑机器人与机器智能研究所）；School of Computation, Information and Technology, Technical University of Munich（计算、信息与技术学院，慕尼黑技术大学）
Leveraging Classic Deconvolution and Feature Extraction in Zero-Shot Image Restoration
Technical University of Munich（慕尼黑工业大学）；Helmholtz AI, Helmholtz Munich - German Research Center for Environmental Health（亥姆霍兹人工智能平台，亥姆霍兹慕尼黑中心——德国环境健康研究中心）；Georg-August-University Göttingen（哥廷根乔治-奥古斯特大学）；GEOMAR Helmholtz Centre for Ocean Research Kiel（基尔亥姆霍兹海洋研究中心）
