159 research outputs found

    Fully Point-wise Convolutional Neural Network for Modeling Statistical Regularities in Natural Images

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    Modeling statistical regularity plays an essential role in ill-posed image processing problems. Recently, deep learning based methods have been presented to implicitly learn statistical representation of pixel distributions in natural images and leverage it as a constraint to facilitate subsequent tasks, such as color constancy and image dehazing. However, the existing CNN architecture is prone to variability and diversity of pixel intensity within and between local regions, which may result in inaccurate statistical representation. To address this problem, this paper presents a novel fully point-wise CNN architecture for modeling statistical regularities in natural images. Specifically, we propose to randomly shuffle the pixels in the origin images and leverage the shuffled image as input to make CNN more concerned with the statistical properties. Moreover, since the pixels in the shuffled image are independent identically distributed, we can replace all the large convolution kernels in CNN with point-wise (1∗11*1) convolution kernels while maintaining the representation ability. Experimental results on two applications: color constancy and image dehazing, demonstrate the superiority of our proposed network over the existing architectures, i.e., using 1/10∼\sim1/100 network parameters and computational cost while achieving comparable performance.Comment: 9 pages, 7 figures. To appear in ACM MM 201

    Does Haze Removal Help CNN-based Image Classification?

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    Hazy images are common in real scenarios and many dehazing methods have been developed to automatically remove the haze from images. Typically, the goal of image dehazing is to produce clearer images from which human vision can better identify the object and structural details present in the images. When the ground-truth haze-free image is available for a hazy image, quantitative evaluation of image dehazing is usually based on objective metrics, such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM). However, in many applications, large-scale images are collected not for visual examination by human. Instead, they are used for many high-level vision tasks, such as automatic classification, recognition and categorization. One fundamental problem here is whether various dehazing methods can produce clearer images that can help improve the performance of the high-level tasks. In this paper, we empirically study this problem in the important task of image classification by using both synthetic and real hazy image datasets. From the experimental results, we find that the existing image-dehazing methods cannot improve much the image-classification performance and sometimes even reduce the image-classification performance

    Recovering Depth from Still Images for Underwater Dehazing Using Deep Learning

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    Estimating depth from a single image is a challenging problem, but it is also interestingdue to the large amount of applications, such as underwater image dehazing. In this paper, a newperspective is provided; by taking advantage of the underwater haze that may provide a strong cue tothe depth of the scene, a neural network can be used to estimate it. Using this approach the depthmapcan be used in a dehazing method to enhance the image and recover original colors, offering abetter input to image recognition algorithms and, thus, improving the robot performance duringvision-based tasks such as object detection and characterization of the seafloor. Experiments areconducted on different datasets that cover a wide variety of textures and conditions, while using adense stereo depthmap as ground truth for training, validation and testing. The results show that theneural network outperforms other alternatives, such as the dark channel prior methods and it is ableto accurately estimate depth from a single image after a training stage with depth information
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