DEFormer: DCT-driven Enhancement Transformer for Low-light Image and Dark Vision
Low-light image enhancement restores the colors and details of a single image and improves high-level visual tasks. However, restoring the lost details in the dark area is still a challenge relying only on the RGB domain. In this paper, we delve into frequency as a new clue into the model and propose a DCT-driven enhancement transformer (DEFormer) framework. First, we propose a learnable frequency branch (LFB) for frequency enhancement contains DCT processing and curvature-based frequency enhancement (CFE) to represent frequency features. Additionally, we propose a cross domain fusion (CDF) to reduce the differences between the RGB domain and the frequency domain. Our DEFormer has achieved superior results on the LOL and MIT-Adobe FiveK datasets, improving the dark detection performance.
Code (0)
등록된 구현이 없습니다.
Tasks
Autonomous DrivingImage EnhancementLow-Light Image EnhancementSimilar Papers 제목 키워드 기반
Deformable Mesh Transformer for 3D Human Mesh Recovery
We present Deformable mesh transFormer (DeFormer), a novel vertex-based approach to monocular 3D human mesh recovery. DeFormer iteratively fits a body mesh model to an input image via a mesh alignment feedback loop f…
3D Hand Pose Estimation3D Human Pose EstimationDecoderHuman Mesh RecoveryDeFormer: Integrating Transformers with Deformable Models for 3D Shape Abstraction from a Single Image
Accurate 3D shape abstraction from a single 2D image is a long-standing problem in computer vision and graphics. By leveraging a set of primitives to represent the target shape, recent methods have achieved promising res…
Does Head Pose Correction Improve Biometric Facial Recognition?
Biometric facial recognition models often demonstrate significant decreases in accuracy when processing real-world images, often characterized by poor quality, non-frontal subject poses, and subject occlusions. We invest…
3D ReconstructionImage RestorationGuideFormer: Transformers for Image Guided Depth Completion
Depth completion has been widely studied to predict a dense depth image from its sparse measurement and a single color image. However, most state-of-the-art methods rely on static convolutional neural networks (CNNs)…
Depth CompletionDeFormer: Decomposing Pre-trained Transformers for Faster Question Answering
Transformer-based QA models use input-wide self-attention -- i.e. across both the question and the input passage -- at all layers, causing them to be slow and memory-intensive. It turns out that we can get by without inp…
Question Answering