This paper presents a new solution for low-light image enhancement by collectively
exploiting Signal-to-Noise-Ratio-aware transformers and convolutional models to …

This paper proposes a new framework for low-light image enhancement by simultaneously
conducting the appearance as well as structure modeling. It employs the structural feature to …

In this paper, we introduce a new approach for high-quality multi-exposure image fusion
(MEF). We show that the fusion weights of an exposure can be encoded into a 1D lookup …

Abstract The 3D Lookup Table (3D LUT) is a highly-efficient tool for real-time image
enhancement tasks, which models a non-linear 3D color transform by sparsely sampling it …

Low-light image enhancement (LLIE) aims to recover illumination and improve the visibility
of low-light images. Conventional LLIE methods often produce poor results because they …

This work targets at using a general deep learning framework to synthesize free-viewpoint
images of arbitrary human performers, only requiring a sparse number of camera views as …

In this paper, we present a Neural Preset technique to address the limitations of existing
color style transfer methods, including visual artifacts, vast memory requirement, and slow …

Image-adaptive lookup tables (LUTs) have achieved great success in real-time image
enhancement tasks due to their high efficiency for modeling color transforms. However, they …

Lookup table (LUT) has shown its efficacy in low-level vision tasks due to the valuable
characteristics of low computational cost and hardware independence. However, recent …

Neural radiance fields have made a remarkable breakthrough in the novel view synthesis
task at the 3D static scene. However, for the 4D circumstance (eg, dynamic scene), the …