Light Source Point Cluster Selection Based Atmosphere Light Estimation
Atmosphere light value is a highly critical parameter in defogging algorithms that are based on an atmosphere scattering model. Any error in atmosphere light value will produce a direct impact on the accuracy of scattering computation and thus bring chromatic distortion to restored images. To address this problem, this paper propose a method that relies on clustering statistics to estimate atmosphere light value. It starts by selecting in the original image some potential atmosphere light source points, which are grouped into point clusters by means of clustering technique. From these clusters, a number of clusters containing candidate atmosphere light source points are selected, the points are then analyzed statistically, and the cluster containing the most candidate points is used for estimating atmosphere light value. The mean brightness vector of the candidate atmosphere light points in the chosen point cluster is taken as the estimate of atmosphere light value, while their geometric center in the image is accepted as the location of atmosphere light. Experimental results suggest that this statistics clustering method produces more accurate atmosphere brightness vectors and light source locations. This accuracy translates to, from a subjective perspective, more natural defogging effect on the one hand and to the improvement in various objective image quality indicators on the other hand.
Code (0)
등록된 구현이 없습니다.
Tasks
ClusteringSimilar Papers 제목 키워드 기반
Clustering of illustrations by atmosphere using a combination of supervised and unsupervised learning
The distribution of illustrations on social media, such as Twitter and Pixiv has increased with the growing popularity of animation, games, and animated movies. The "atmosphere" of illustrations plays an important role i…
ClusteringNeural Importance Sampling of Many Lights
We propose a neural approach for estimating spatially varying light selection distributions to improve importance sampling in Monte Carlo rendering, particularly for complex scenes with many light sources. Our method use…
Quasi Static Atmospheric Model for Aircraft Trajectory Prediction and Flight Simulation
Aircraft trajectory prediction requires the determination of the atmospheric properties (pressure, temperature, and density) encountered by the aircraft during its flight. This is accomplished by employing a tabulated pr…
Trajectory PredictionA new method of carbon budget analysis with application to present and paleo atmospheric concentration data sets
We introduce a new method for analyzing the carbon budget using box models and a mass balance approach. The method describes the net flow of carbon between the atmosphere and other reservoirs. The method assumes that the…
High-precision interpolation of stellar atmospheres with a deep neural network using a 1D convolutional auto encoder for feature extraction
Given the widespread availability of grids of models for stellar atmospheres, it is necessary to recover intermediate atmospheric models by means of accurate techniques that go beyond simple linear interpolation and capt…