A Brief Review of Real-World Color Image Denoising
원문 초록
Filtering real-world color images is challenging due to the complexity of noise that can not be formulated as a certain distribution. However, the rapid development of camera lens pos- es greater demands on image denoising in terms of both efficiency and effectiveness. Currently, the most widely accepted framework employs the combination of transform domain techniques and nonlocal similarity characteristics of natural images. Based on this framework, many competitive methods model the correlation of R, G, B channels with pre-defined or adaptively learned transforms. In this chapter, a brief review of related methods and publicly available datasets is presented, moreover, a new dataset that includes more natural outdoor scenes is introduced. Extensive experiments are performed and discussion on visual effect enhancement is included.
함께 살펴볼 논문
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Color Image Restoration Exploiting Inter-channel Correlation with a 3-stage CNN
Energy-Based Transformers are Scalable Learners and Thinkers
STAR-Net: An Interpretable Model-Aided Network for Remote Sensing Image Denoising
Optimal Density Functions for Weighted Convolution in Learning Models
Optimal Weighted Convolution for Classification and Denosing
Rotation-Equivariant Self-Supervised Method in Image Denoising
토론
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