We propose a novel deep neural network architecture for the challenging problem of single image dehazing, which aims to recover the clear image from a degraded hazy image. Instead of relying on hand-crafted image priors or explicitly estimating the components of the widely used atmospheric scattering model, our end-to-end system directly generates the clear image from an input hazy image. The proposed network has an encoder-decoder architecture with skip connections and instance normalization. We adopt the convolutional layers of the pre-trained VGG network as encoder to exploit the representation power of deep features, and demonstrate the effectiveness of instance normalization for image dehazing. Our simple yet effective network outperforms the state-of-the-art methods by a large margin on the benchmark datasets.
Traditional methods focus on representing human knowledge as priors for image processing. assumes higher contrast of clear images and proposes a patch-based contrast-maximization method. assumes the transmission and surface shading are locally uncorrelated, and estimates the albedo of the scene. Dark channel prior (DCP) @cite_11 assumes local patches contain low intensity pixels in at lease one color channel and hence estimates the transmission map. Fast visibility restoration (FVR) @cite_5 is a filtering approach by atmospheric veil inference and corner preserving smoothing. uses boundary constraint and contextual regularization (BCCR), and uses gradient residual minimization (GRM) to surpress artifacts. combines priors by learning with random forests model. Color attenuation prior (CAP) @cite_29 assumes a linear model of brightness and the saturation and then learns the coefficients. assumes each color cluster in the clear image becomes a line in RGB space, and proposes non-local image dehazing (NLD).
@cite_5 @cite_29 @cite_11
2518979500 2462462929 2240914251 2798876216
Most existing image dehazing methods tend to boost local image contrast for regions with heavy haze. Without special treatment, these methods may significantly amplify existing image artifacts such as noise, color aliasing and blocking, which are mostly invisible in the input images but are visually intruding in the results. This is especially the case for low quality cellphone shots or compressed video frames. The recent work of (2014) addresses blocking artifacts for dehazing, but is insufficient to handle other artifacts. In this paper, we propose a new method for reliable suppression of different types of visual artifacts in image and video dehazing. Our method makes contributions in both the haze estimation step and the image recovery step. Firstly, an image-guided, depth-edge-aware smoothing algorithm is proposed to refine the initial atmosphere transmission map generated by local priors. In the image recovery process, we propose Gradient Residual Minimization (GRM) for jointly recovering the haze-free image while explicitly minimizing possible visual artifacts in it. Our evaluation suggests that the proposed method can generate results with much less visual artifacts than previous approaches for lower quality inputs such as compressed video clips. In this paper, we propose a non-local structured prior for volumetric multi-view 3D reconstruction. Towards this goal, we present a novel Markov random field model based on ray potentials in which assumptions about large 3D surface patches such as planarity or Manhattan world constraints can be efficiently encoded as probabilistic priors. We further derive an inference algorithm that reasons jointly about voxels, pixels and image segments, and estimates marginal distributions of appearance, occupancy, depth, normals and planarity. Key to tractable inference is a novel hybrid representation that spans both voxel and pixel space and that integrates non-local information from 2D image segmentations in a principled way. We compare our non-local prior to commonly employed local smoothness assumptions and a variety of state-of-the-art volumetric reconstruction baselines on challenging outdoor scenes with textureless and reflective surfaces. Our experiments indicate that regularizing over larger distances has the potential to resolve ambiguities where local regularizers fail. The restoration of clean remote sensing images from the degraded images is usually an ill-posed problem. A feasible solution for the problem is incorporating various priors into restoration procedure as constrained conditions. Recently, the patch-based Gaussian mixture model (GMM) priors became popular. However, the learning of patch-based GMM priors usually assumes the components of the GMM are independent and identically distributed (i.i.d.). The assumption means that two components could model the same training samples, and thus a possible decrease in model’s description ability. This work presents a new learning method to diversify the patch-based GMM priors through modeling the GMM’s parameters as a determinantal point process (DPP). The introduction of DPP allows us to specify a preference for diversity in patch-based GMM using a positive definite kernel function and thus to increase the model’s description ability for a given model complexity. Furthermore, under the expected patch log-likelihood (EPLL) restoration framework, we further introduce the diversified patch-based GMM priors into remote sensing literature and develop new remote sensing image restoration algorithms. The extensive experiments for denoising and deblurring of remote sensing images demonstrated that the proposed algorithms produced better restoration results than other methods that already proven to perform well in the literature. In this paper, we present an algorithm to directly restore a clear image from a hazy image. This problem is highly ill-posed and most existing algorithms often use hand-crafted features, e.g., dark channel, color disparity, maximum contrast, to estimate transmission maps and then atmospheric lights. In contrast, we solve this problem based on a conditional generative adversarial network (cGAN), where the clear image is estimated by an end-to-end trainable neural network. Different from the generative network in basic cGAN, we propose an encoder and decoder architecture so that it can generate better results. To generate realistic clear images, we further modify the basic cGAN formulation by introducing the VGG features and an L1-regularized gradient prior. We also synthesize a hazy dataset including indoor and outdoor scenes to train and evaluate the proposed algorithm. Extensive experimental results demonstrate that the proposed method performs favorably against the state-of-the-art methods on both synthetic dataset and real world hazy images.