Papers Color Constancy
“Color Constancy” 태그가 달린 논문 114편 · 필터 해제
Lesion Elevation Prediction from Skin Images Improves Diagnosis
While deep learning-based computer-aided diagnosis for skin lesion image analysis is approaching dermatologists' performance levels, there are several works showing that incorporating additional features such as shape pr…
Color ConstancyDomain GeneralizationPredictionSimple Image Signal Processing using Global Context Guidance
In modern smartphone cameras, the Image Signal Processor (ISP) is the core element that converts the RAW readings from the sensor into perceptually pleasant RGB images for the end users. The ISP is typically proprietary …
Color ConstancyTone MappingSelf-Supervised Learning of Color Constancy
Color constancy (CC) describes the ability of the visual system to perceive an object as having a relatively constant color despite changes in lighting conditions. While CC and its limitations have been carefully charact…
Color ConstancySelf-Supervised LearningOptimizing Illuminant Estimation in Dual-Exposure HDR Imaging
High dynamic range (HDR) imaging involves capturing a series of frames of the same scene, each with different exposure settings, to broaden the dynamic range of light. This can be achieved through burst capturing or usin…
Color ConstancyPixel-Wise Color Constancy via Smoothness Techniques in Multi-Illuminant Scenes
Most scenes are illuminated by several light sources, where the traditional assumption of uniform illumination is invalid. This issue is ignored in most color constancy methods, primarily due to the complex spatial impac…
Color ConstancyNightCC: Nighttime Color Constancy via Adaptive Channel Masking
Nighttime conditions pose a significant challenge to color constancy due to the diversity of lighting conditions and the presence of substantial low-light noise. Existing color constancy methods struggle with nightti…
Color ConstancyDiversityDomain AdaptationUnsupervised Domain AdaptationInvestigating Color Illusions from the Perspective of Computational Color Constancy
Color constancy and color illusion perception are two phenomena occurring in the human visual system, which can help us reveal unknown mechanisms of human perception. For decades computer vision scientists have developed…
Color ConstancyPractical cross-sensor color constancy using a dual-mapping strategy
Deep Neural Networks (DNNs) have been widely used for illumination estimation, which is time-consuming and requires sensor-specific data collection. Our proposed method uses a dual-mapping strategy and only requires a si…
Color ConstancyCPUGPUFooling Polarization-based Vision using Locally Controllable Polarizing Projection
Polarization is a fundamental property of light that encodes abundant information regarding surface shape, material, illumination and viewing geometry. The computer vision community has witnessed a blossom of polarizatio…
Color ConstancyReflection RemovalSemantic SegmentationMIMT: Multi-Illuminant Color Constancy via Multi-Task Local Surface and Light Color Learning
The assumption of a uniform light color distribution is no longer applicable in scenes that have multiple light colors. Most color constancy methods are designed to deal with a single light color, and thus are erroneous …
Color ConstancyEdge DetectionMulti-Task LearningTemplate matching with white balance adjustment under multiple illuminants
In this paper, we propose a novel template matching method with a white balancing adjustment, called N-white balancing, which was proposed for multi-illuminant scenes. To reduce the influence of lighting effects, N-white…
Color Constancyobject-detectionObject DetectionTemplate MatchingDynamic Dense RGB-D SLAM using Learning-based Visual Odometry
We propose a dense dynamic RGB-D SLAM pipeline based on a learning-based visual odometry, TartanVO. TartanVO, like other direct methods rather than feature-based, estimates camera pose through dense optical flow, which o…
Color ConstancyOptical Flow EstimationVisual OdometryDesigning Perceptual Puzzles by Differentiating Probabilistic Programs
We design new visual illusions by finding "adversarial examples" for principled models of human perception -- specifically, for probabilistic models, which treat vision as Bayesian inference. To perform this search effic…
Bayesian InferenceColor ConstancyProbabilistic ProgrammingLearning Enriched Illuminants for Cross and Single Sensor Color Constancy
Color constancy aims to restore the constant colors of a scene under different illuminants. However, due to the existence of camera spectral sensitivity, the network trained on a certain sensor, cannot work well on other…
Color ConstancyDiversityModel-Based Image Signal Processors via Learnable Dictionaries
Digital cameras transform sensor RAW readings into RGB images by means of their Image Signal Processor (ISP). Computational photography tasks such as image denoising and colour constancy are commonly performed in the RAW…
Color ConstancyData AugmentationDenoisingImage Denoising+3Degree-of-Linear-Polarization-Based Color Constancy
Color constancy is an essential function in digital photography and a fundamental process for many computer vision applications. Accordingly, many methods have been proposed, and some recent ones have used deep neura…
Color ConstancyPoint Cloud Color Constancy
In this paper, we present Point Cloud Color Constancy, in short PCCC, an illumination chromaticity estimation algorithm exploiting a point cloud. We leverage the depth information captured by the time-of-flight (ToF) sen…
Color ConstancyRobust channel-wise illumination estimation
Recently, Convolutional Neural Networks (CNNs) have been widely used to solve the illuminant estimation problem and have often led to state-of-the-art results. Standard approaches operate directly on the input image. In …
Color ConstancyGenerative Models for Multi-Illumination Color Constancy
In this paper, the aim is multi-illumination color constancy. However, most of the existing color constancy methods are designed for single light sources. Furthermore, datasets for learning multiple illumination color co…
Color ConstancyData AugmentationTranslationCLCC: Contrastive Learning for Color Constancy
In this paper, we present CLCC, a novel contrastive learning framework for color constancy. Contrastive learning has been applied for learning high-quality visual representations for image classification. One key aspect …
Color ConstancyContrastive Learningimage-classificationImage Classification