arXiv:2101.02802
Single Image Super-Resolution
원문 초록
This study presents a chronological overview of the single image super-resolution problem. We first define the problem thoroughly and mention some of the serious challenges. Then the problem formulation and the performance metrics are defined. We give an overview of the previous methods relying on reconstruction based solutions and then continue with the deep learning approaches. We pick 3 landmark architectures and present their results quantitatively. We see that the latest proposed network gives favorable output compared to the previous methods.
함께 살펴볼 논문
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IM-LUT: Interpolation Mixing Look-Up Tables for Image Super-Resolution
Structural Similarity-Inspired Unfolding for Lightweight Image Super-Resolution
DACN: Dual-Attention Convolutional Network for Hyperspectral Image Super-Resolution
A Tree-guided CNN for image super-resolution
DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-Resolution
Trustworthy Image Super-Resolution via Generative Pseudoinverse
토론
이 논문과 연결된 포럼 글이 아직 없습니다.