Removing Rain From a Single Image via Discriminative Sparse Coding
Visual distortions on images caused by bad weather conditions can have a negative impact on the performance of many outdoor vision systems. One often seen bad weather is rain which causes significant yet complex local intensity fluctuations in images. The paper aims at developing an effective algorithm to remove visual effects of rain from a single rainy image, i.e. separate the rain layer and the de-rained image layer from an rainy image. Built upon a non-linear generative model of rainy image, namely screen blend mode, we proposed a dictionary learning based algorithm for single image de-raining. The basic idea is to sparsely approximate the patches of two layers by very high discriminative codes over a learned dictionary with strong mutual exclusivity property. Such discriminative sparse codes lead to accurate separation of two layers from their non-linear composite. The experiments showed that the proposed method outperformed the existing single image de-raining methods on tested rain images.
Code (0)
등록된 구현이 없습니다.
Tasks
Dictionary LearningRain RemovalSimilar Papers 제목 키워드 기반
SDGM: Sparse Bayesian Classifier Based on a Discriminative Gaussian Mixture Model
In probabilistic classification, a discriminative model based on Gaussian mixture exhibits flexible fitting capability. Nevertheless, it is difficult to determine the number of components. We propose a sparse classifier …
Joint Bi-Layer Optimization for Single-Image Rain Streak Removal
We present a novel method for removing rain streaks from a single input image by decomposing it into a rain-free background layer B and a rain-streak layer R. A joint optimization process is used that alternates between …
Rain RemovalAdvMask: A Sparse Adversarial Attack Based Data Augmentation Method for Image Classification
Data augmentation is a widely used technique for enhancing the generalization ability of convolutional neural networks (CNNs) in image classification tasks. Occlusion is a critical factor that affects on the generalizati…
Adversarial AttackClassificationData Augmentationimage-classification+1Sparse Coding for Classification via Discrimination Ensemble
Discriminative sparse coding has emerged as a promising technique in image analysis and recognition, which couples the process of classifier training and the process of dictionary learning for improving the discriminabil…
ClassificationDictionary LearningGeneral ClassificationLearning Discriminative Reconstructions for Unsupervised Outlier Removal
We study the problem of automatically removing outliers from noisy data, with application for removing outlier images from an image collection. We address this problem by utilizing the reconstruction errors of an autoenc…