Image dehazing and deraining using Opening-Closing Network, a very novel kind of morphological neural network.
Ranjan Mondal, Moni Shankar Dey & Bhabatosh Chanda, Mathematical Morphology-Theory and Applications 4 (1), 87–107.
Outdoor images are used to train algorithms for various tasks, such as autonomous navigation and surveillance. As the imaging systems interact with their environment, they are exposed to a wide variety of weather conditions such as rain, haze or fog. This may result in colour or contrast degradation, which produces a low-quality image. These type of images need to be restored to their original state by removing noises before they are deemed suitable to be used in computer vision-based tasks. Image de-hazing and de-raining are highly challenging tasks and are some of the most widely researched topics in the field of image restoration.
Classical computer vision (CV) algorithms rely on developing handcrafted features, borrowing concepts from diverse fields such as set theory, statistical physics and signal processing, for image analysis. Deep learning (DL) based methods are widely used nowadays to solve challenging problems in image processing such as segmentation, classification and detection. Unlike traditional CV techniques, high-level abstract features relevant to the dataset are extracted automatically. Although DL algorithms achieve higher accuracy, the higher level abstract features are often hard to interpret, which results in their black-box like behavior.
There is ongoing research to design a high performing model with better transparency, combining the advantages of both CV and DL techniques. In this article, we briefly describe our paper  in which we use the novel Opening-Closing network, a type of morphological neural network, for image de-hazing and de-raining.
Deep learning on graphs: successes, challenges, and next steps. TL;DR This is the first in a series of posts where I will discuss the evolution and future trends in the field of deep learning on graphs.
Changing Image Backgrounds Using Image Segmentation. Make the world your green screen. Hello readers! In this article I’ll be discussing another cool trick we can do with deep learning.
Everything we see around its nothing but an Image. we capture them using our mobile camera. Image is nothing but a signal which conveys.
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Being a practitioner in Machine Learning, you must have gone through an image classification, where the goal is to assign a label or a class to the input image.