Contrast Restoration of Weather Degraded Images

By Srinivasa G. Narasimhan, Shree K. Nayar. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003

1,683 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/442c6559f5319db78d9888d23277ea540a3e0bb8

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Citations in the corpus

BLIND CONTRAST ENHANCEMENT ASSESSMENT BY GRADIENT RATIOING AT VISIBLE EDGES (2011, methodology): “As a consequence, different methods have been designed to restore their contrast, in order to maintain the performances of video-surveillance systems (Narasimhan and Nayar, 2003) or in-vehicle vision systems (Hautière et al.”
A retinex-based enhancing approach for single underwater image (2014, background): “A similar approach is introduced in [5] which use multiple images to restore the contrast.”
Fast visibility restoration from a single color or gray level image (2009, methodology): “As a consequence, approaches based on the use of several images of the scene were proposed: using images at different times [8] or using images with different polarizing filters [11].”
DEA-Net: Single Image Dehazing Based on Detail-Enhanced Convolution and Content-Guided Attention (2023, methodology): “Earlier CNN-based methods [2], [7], [8] first estimate the transmission map and the atmospheric light separately, and then utilize the Atmospheric Scattering Model (ASM) [9] to derive the haze-free images.”
LIME: Low-Light Image Enhancement via Illumination Map Estimation (2017, methodology): “As mentioned, another widely used model is based on the observation that inverted low-light images 1−L look similar to haze images, which is thus expressed as [20], [21], [22]:”
Removing Rain from a Single Image via Discriminative Sparse Coding (2015, background): “In the last few decades, there has been an abundant literature on recovering outdoor images related to bad weath-ers, e.g . image recovery for atmospherically blurred images [25], contrast enhancement of weather degraded images [16], and removing haze from images [12].”
Single Image Haze Removal Using Dark Channel Prior (2011, background): “In [5], [6], [7], more constraints are obtained from multiple images of the same scene under different weather conditions.”
Referenceless Prediction of Perceptual Fog Density and Perceptual Image Defogging (2015, background): “1) Foggy Image Formation: Accurate modeling of optical scattering is a complex problem that is complicated by the wide variety of types, sizes, orientations, and distributions of particles constituting a media, as well as the wavelength direction of the ambient incident light, and the polarization s”
Investigating Haze-Relevant Features in a Learning Framework for Image Dehazing (2014, background): “Nayar and Narasimhan [14] [13] also capture multiple images of the same scene and use the differences of images for estimating the haze properties.”
Cycle-Dehaze: Enhanced CycleGAN for Single Image Dehazing (2018, methodology): “Multiple images based methods overcome dehazing problem by obtaining changed atmospheric conditions from multiple images [21–23, 36].”
NH-HAZE: An Image Dehazing Benchmark with Non-Homogeneous Hazy and Haze-Free Images (2020, background): “Image dehazing is an ill-posed problem that has been solved initially based on additional information such as atmospheric cues [18, 32], multiple images captured with polarization filters [33, 37], or known depth information [25, 40].”
An Underwater Image Enhancement Benchmark Dataset and Beyond (2019, background): “stage, supplementary information from multiple images [17] or specialize hardware devices (e.”
D-HAZY: A dataset to evaluate quantitatively dehazing algorithms (2016, background): “While earlier dehazing approaches employ additional information such as multiple images [3] or a rough estimate of the depth [4], recent techniques have tackled this problem by using only the information of a single hazy input image [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16].”
Color Balance and Fusion for Underwater Image Enhancement (2018, methodology): “B, the problem has been tackled by tailored acquisition strategies using multiple images [9], specialized hardware [10] or polarization filters [11].”
A Fast Single Image Haze Removal Algorithm Using Color Attenuation Prior (2015, background): “(the direct attenuation and the airlight) in imaging under hazy weather [23].”
Benchmarking Single-Image Dehazing and Beyond (2017, background): “While some earlier works consider multiple images from the same scene to be available for dehazing [2], [3], [4], [5], the single image dehazing proves to be a more realistic setting in practice, and thus gained the dominant popularity.”
Single Image Dehazing via Multi-scale Convolutional Neural Networks with Holistic Edges (2019, methodology): “As the image dehazing problem is ill-posed, early approaches often require multiple frames to deal with this problem (Treibitz and Schechner 2009; Narasimhan and Nayar 2003, 2000; Schechner et al. 2001; Shwartz et al. 2006).”
Semantic Foggy Scene Understanding with Synthetic Data (2017, neutral): “Fog degrades the visibility of a scene signiﬁ-cantly [48,62].”