Image Dehazing via Joint Estimation of Transmittance Map and Environmental Illumination
원문 초록
Haze limits the visibility of outdoor images, due to the existence of fog, smoke and dust in the atmosphere. Image dehazing methods try to recover haze-free image by removing the effect of haze from a given input image. In this paper, we present an end to end system, which takes a hazy image as its input and returns a dehazed image. The proposed method learns the mapping between a hazy image and its corresponding transmittance map and the environmental illumination, by using a multi-scale Convolutional Neural Network. Although most of the time haze appears grayish in color, its color may vary depending on the color of the environmental illumination. Very few of the existing image dehazing methods have laid stress on its accurate estimation. But the color of the dehazed image and the estimated transmittance depends on the environmental illumination. Our proposed method exploits the relationship between the transmittance values and the environmental illumination as per the haze imaging model and estimates both of them. Qualitative and quantitative evaluations show, the estimates are accurate enough.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Forward-only Diffusion Probabilistic Models
Degradation-Aware Feature Perturbation for All-in-One Image Restoration
A Preliminary Study for GPT-4o on Image Restoration
WDMamba: When Wavelet Degradation Prior Meets Vision Mamba for Image Dehazing
Fine-Tuning Adversarially-Robust Transformers for Single-Image Dehazing
Learning Hazing to Dehazing: Towards Realistic Haze Generation for Real-World Image Dehazing
토론
이 논문과 연결된 포럼 글이 아직 없습니다.