Papers Multi-Exposure Image Fusion
“Multi-Exposure Image Fusion” 태그가 달린 논문 36편 · 필터 해제
Retinex-MEF: Retinex-based Glare Effects Aware Unsupervised Multi-Exposure Image Fusion
Multi-exposure image fusion consolidates multiple low dynamic range images of the same scene into a singular high dynamic range image. Retinex theory, which separates image illumination from scene reflectance, is natural…
Multi-Exposure Image FusionMulti-Exposure Image Fusion via Distilled 3D LUT Grid with Editable Mode
With the rising imaging resolution of handheld devices, existing multi-exposure image fusion algorithms struggle to generate a high dynamic range image with ultra-high resolution in real-time. Apart from that, there is a…
Multi-Exposure Image FusionUltra-High-Definition Dynamic Multi-Exposure Image Fusion via Infinite Pixel Learning
With the continuous improvement of device imaging resolution, the popularity of Ultra-High-Definition (UHD) images is increasing. Unfortunately, existing methods for fusing multi-exposure images in dynamic scenes are des…
GPULarge Language ModelMulti-Exposure Image FusionScene-Segmentation-Based Exposure Compensation for Tone Mapping of High Dynamic Range Scenes
We propose a novel scene-segmentation-based exposure compensation method for multi-exposure image fusion (MEF) based tone mapping. The aim of MEF-based tone mapping is to display high dynamic range (HDR) images on device…
Multi-Exposure Image FusionScene SegmentationTone MappingLittle Strokes Fell Great Oaks: Boosting the Hierarchical Features for Multi-exposure Image Fusion
In recent years, deep learning networks have made remarkable strides in the domain of multi-exposure image fusion. Nonetheless, prevailing approaches often involve directly feeding over-exposed and under-exposed images i…
Multi-Exposure Image FusionFusionINN: Decomposable Image Fusion for Brain Tumor Monitoring
Image fusion typically employs non-invertible neural networks to merge multiple source images into a single fused image. However, for clinical experts, solely relying on fused images may be insufficient for making diagno…
DenoisingDiagnosticMulti-Exposure Image FusionBayesian multi-exposure image fusion for robust high dynamic range ptychography
The limited dynamic range of the detector can impede coherent diffractive imaging (CDI) schemes from achieving diffraction-limited resolution. To overcome this limitation, a straightforward approach is to utilize high dy…
Multi-Exposure Image FusionRetrievalA Dual Domain Multi-exposure Image Fusion Network based on the Spatial-Frequency Integration
Multi-exposure image fusion aims to generate a single high-dynamic image by integrating images with different exposures. Existing deep learning-based multi-exposure image fusion methods primarily focus on spatial domain …
Multi-Exposure Image FusionReFusion: Learning Image Fusion from Reconstruction with Learnable Loss via Meta-Learning
Image fusion aims to combine information from multiple source images into a single one with more comprehensive informational content. The significant challenges for deep learning-based image fusion algorithms are the lac…
Meta-LearningMulti-Exposure Image FusionMEFLUT: Unsupervised 1D Lookup Tables for Multi-exposure Image Fusion
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 table (LUT), which takes pixel intensity value…
4kGPUMulti-Exposure Image FusionEMEF: Ensemble Multi-Exposure Image Fusion
Although remarkable progress has been made in recent years, current multi-exposure image fusion (MEF) research is still bounded by the lack of real ground truth, objective evaluation function, and robust fusion strategy.…
Multi-Exposure Image FusionSearching a Compact Architecture for Robust Multi-Exposure Image Fusion
In recent years, learning-based methods have achieved significant advancements in multi-exposure image fusion. However, two major stumbling blocks hinder the development, including pixel misalignment and inefficient infe…
Multi-Exposure Image FusionNeural Architecture SearchExposure Fusion for Hand-held Camera Inputs with Optical Flow and PatchMatch
This paper proposes a hybrid synthesis method for multi-exposure image fusion taken by hand-held cameras. Motions either due to the shaky camera or caused by dynamic scenes should be compensated before any content fusion…
Multi-Exposure Image FusionOptical Flow EstimationSuperpixelsSelf-FuseNet: Data Free Unsupervised Remote Sensing Image Super-Resolution
Real-world degradations deviate from ideal degradations, as most deep learning-based scenarios involve the ideal synthesis of low-resolution (LR) counterpart images by popularly used bicubic interpolation. Moreover, supe…
Blind Super-ResolutionFeature EngineeringFeature ImportanceImage Enhancement+7Perceptual Multi-Exposure Fusion
As an ever-increasing demand for high dynamic range (HDR) scene shooting, multi-exposure image fusion (MEF) technology has abounded. In recent years, multi-scale exposure fusion approaches based on detail-enhancement hav…
Image EnhancementMulti-Exposure Image FusionSSIMDeep Unrolled Low-Rank Tensor Completion for High Dynamic Range Imaging
The major challenge in high dynamic range (HDR) imaging for dynamic scenes is suppressing ghosting artifacts caused by large object motions or poor exposures. Whereas recent deep learning-based approaches have shown sign…
HDR ReconstructionImage GenerationMulti-Exposure Image FusionVariational Approach for Intensity Domain Multi-exposure Image Fusion
Recent innovations shows that blending of details captured by single Low Dynamic Range (LDR) sensor overcomes the limitations of standard digital cameras to capture details from high dynamic range scene. We present a met…
Multi-Exposure Image FusionA Perceptually Optimized and Self-Calibrated Tone Mapping Operator
With the increasing popularity and accessibility of high dynamic range (HDR) photography, tone mapping operators (TMOs) for dynamic range compression are practically demanding. In this paper, we develop a two-stage neura…
Multi-Exposure Image FusionSSIMTone MappingTransFuse: A Unified Transformer-based Image Fusion Framework using Self-supervised Learning
Image fusion is a technique to integrate information from multiple source images with complementary information to improve the richness of a single image. Due to insufficient task-specific training data and corresponding…
DecoderMulti-Exposure Image FusionMulti Focus Image FusionSelf-Supervised LearningEfficient joint noise removal and multi exposure fusion
Multi-exposure fusion (MEF) is a technique for combining different images of the same scene acquired with different exposure settings into a single image. All the proposed MEF algorithms combine the set of images, someho…
DenoisingMulti-Exposure Image Fusion