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Found 5 papers, 1 papers with code
CRS-Diff: Controllable Generative Remote Sensing Foundation Model
The emergence of diffusion models has revolutionized the field of image generation, providing new methods for creating high-quality, high-resolution images across various applications.
Dual Degradation-Inspired Deep Unfolding Network for Low-Light Image Enhancement
Although low-light image enhancement has achieved great stride based on deep enhancement models, most of them mainly stress on enhancement performance via an elaborated black-box network and rarely explore the physical significance of enhancement models.
Division Gets Better: Learning Brightness-Aware and Detail-Sensitive Representations for Low-Light Image Enhancement
Low-light image enhancement strives to improve the contrast, adjust the visibility, and restore the distortion in color and texture.
Sparsity and Coefficient Permutation Based Two-Domain AMP for Image Block Compressed Sensing
In this paper, to overcome the issues and develop a high-performance LDAMP method for image block compressed sensing (BCS), we propose a novel sparsity and coefficient permutation-based AMP (SCP-AMP) method consisting of the block-based sampling and the two-domain reconstruction modules.
Absolute Zero-Shot Learning
Considering the increasing concerns about data copyright and privacy issues, we present a novel Absolute Zero-Shot Learning (AZSL) paradigm, i. e., training a classifier with zero real data.
