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Chapter and Conference Paper
Correction to: A Very Deep Adaptive Convolutional Neural Network (VDACNN) for Image Dehazing
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Chapter and Conference Paper
A Fast Image Dehazing Using Encoder–Decoder Deep Neural Network
The image quality is degraded in bad weather situations such as haze or fog. This problem can affect image processing applications such as computer vision, security, and some other real-time image processing s...
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Chapter and Conference Paper
A Very Deep Adaptive Convolutional Neural Network (VDACNN) for Image Dehazing
The main challenge faced by the existing methods is that they cannot efficiently eliminate the haze from the dense hazy or foggy images. The haze features of dense hazy images are not effectively
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Article
A generic post-processing framework for image dehazing
There are several methods available for image dehazing. The challenges faced by most of these algorithms include under-exposure and leftover haze after dehazing, which eventually leads to low brightness and lo...
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Chapter and Conference Paper
Fast Adaptive Image Dehazing and Details Enhancement of Hazy Images
The majority of the existing methods for image dehazing are of more complexity, which exhibits more time for execution. Therefore, these algorithms may not be suitable for real-time image processing systems. A...
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Chapter and Conference Paper
Image Dehazing Based on Colour Ellipsoid Prior and Low-Light Image Enhancement
The images in hazy environment are not clearly visible due to atmospheric light scattering. Hence, image dehazing is required to reduce the haze effect. In this paper, a colour ellipsoid prior-based model is u...
