Results 311 to 320 of about 7,150,154 (369)

LSDIR: A Large Scale Dataset for Image Restoration

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2023
The aim of this paper is to propose a large scale dataset for image restoration (LSDIR). Recent work in image restoration has been focused on the design of deep neural networks.
Yawei Li, K. Zhang, Jingyun Liang, Jiezhang Cao, Ce Liu, R. Gong, Yulun Zhang, H. Tang, Yun Liu, D. Demandolx, Rakesh Ranjan, Radu Timofte, L. Van Gool   +12 more
semanticscholar   +1 more source

MB-TaylorFormer V2: Improved Multi-Branch Linear Transformer Expanded by Taylor Formula for Image Restoration

IEEE Transactions on Pattern Analysis and Machine Intelligence
Recently, Transformer networks have demonstrated outstanding performance in the field of image restoration due to the global receptive field and adaptability to input.
Zhi Jin, Yuwei Qiu, Kaihao Zhang, Hongdong Li, Wenhan Luo   +4 more
semanticscholar   +1 more source

Image Restoration via Frequency Selection

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023
Image restoration aims to reconstruct the latent sharp image from its corrupted counterpart. Besides dealing with this long-standing task in the spatial domain, a few approaches seek solutions in the frequency domain by considering the large discrepancy ...
Yuning Cui, Wenqi Ren, Xiaochun Cao, Alois Knoll   +3 more
semanticscholar   +1 more source

Non-Uniform Illumination Underwater Image Restoration via Illumination Channel Sparsity Prior

IEEE transactions on circuits and systems for video technology (Print)
Underwater image quality is seriously degraded due to the insufficient light in water. Although artificial illumination can assist imaging, it often brings non-uniform illumination phenomenon.
Guojia Hou, Nan Li, Peixian Zhuang, Kunqian Li, Haihan Sun, Chongyi Li   +5 more
semanticscholar   +1 more source

Image Restoration: Fundamentals of Image Restoration

2014
Abstract Image restoration is the process of recovering an image from a degraded version—usually a blurred and noisy image. Image restoration is a fundamental problem in image processing, and it also provides a testbed for more general inverse problems.
openaire   +1 more source

Image restoration

WIREs Computational Statistics, 2009
AbstractTrue images are usually degraded during image acquisition. Image restoration is for restoring true images from their observed but degraded versions; it is often used for preprocessing observed images so that subsequent image processing and analysis become more reliable.
openaire   +1 more source

InstructIR: High-Quality Image Restoration Following Human Instructions

European Conference on Computer Vision
Image restoration is a fundamental problem that involves recovering a high-quality clean image from its degraded observation. All-In-One image restoration models can effectively restore images from various types and levels of degradation using ...
Marcos V. Conde, Gregor Geigle, Radu Timofte   +2 more
semanticscholar   +1 more source

AdaIR: Adaptive All-in-One Image Restoration via Frequency Mining and Modulation

International Conference on Learning Representations
In the image acquisition process, various forms of degradation, including noise, haze, and rain, are frequently introduced. These degradations typically arise from the inherent limitations of cameras or unfavorable ambient conditions.
Yuning Cui, Syed Waqas Zamir, Salman H. Khan, Alois Knoll, Mubarak Shah, F. Khan   +5 more
semanticscholar   +1 more source

WAVELET-CONSTRAINED IMAGE RESTORATION

International Journal of Wavelets, Multiresolution and Information Processing, 2004
Image restoration problems can naturally be cast as constrained convex programming problems in which the constraints arise from a priori information and the observation of signals physically related to the image to be recovered. In this paper, the focus is placed on the construction of constraints based on wavelet representations.
Combettes, Patrick Louis, Pesquet, Jean-Christophe   +1 more
openaire   +3 more sources

Perceive-IR: Learning to Perceive Degradation Better for All-in-One Image Restoration

IEEE Transactions on Image Processing
Existing All-in-One image restoration methods often fail to simultaneously perceive degradation types and severity levels, overlooking the importance of fine-grained quality perception.
Xu Zhang, Jiaqi Ma, Guoli Wang, Qian Zhang, Huan Zhang, Lefei Zhang   +5 more
semanticscholar   +1 more source