Flowchart For Opencv Project Rain Removal From Rainny Images

Under rainy weather the effect of rain on images and videos is undesirable. Not only do they affect the quality of the images but also degrade the performance of computer vision algorithms. In this project we develop and study several methods to remove rain streaks from single images and show their comparative study.

Fig. 2. Flowchart of the proposed single image rain streaks removal framework. The rainy image is decomposed into the detail layer and the base layer. A detail patch is fed to the parameter network to obtain the angle and the length of the motion blur kernel. The motion blur kernel is stretched to the degr adation map.

For the single image rain removal SIRR task, the performance of deep learning DL-based methods is mainly affected by the designed deraining models and training datasets. Most of current state-of-the-art focus on constructing powerful deep models to obtain better deraining results. In this paper

rainy images using clean images to train the network. After training, the network can be used to output de-rained ver-sions of an input rainy image. 2.1. Network design We denote the input rainy image and corresponding clean image as Xand Y, respectively. Intuitively, a goal may be to directly train a deep CNN architecture hXon

Rain detection and removal using OpenCV. Abstract. The detection of bad weather conditions is crucial for meteorological centers, specially with demand for air, sea and ground traffic management. In this project, a system based on computer vision is presented which detects the presence of rain or snow.

Multi-Scale Progressive Fusion Network for Single Image Deraining. kuihuaMSPFN CVPR 2020 In this work, we explore the multi-scale collaborative representation for rain streaks from the perspective of input image scales and hierarchical deep features in a unified framework, termed multi-scale progressive fusion network MSPFN for single image rain streak removal.

You can solve this problem using image to image translation methods. If you have paired images clean image, and the corresponding rainy image, you can use some paired approaches like Pix2Pix paper github link. The github implementation is easy to adapt to you case, just put your images in the corresponding folders, and lunch the training.

This sounds like an interesting project, where a camera can automatically detect obstruction paint, sticker, rain. It will most likely be necessary for the camera to be mounted without obstructions so that the expected image can be learned. while rain will result in noisy images. OpenCV with C or Python can help solve this kind of

a more efcient way to synthesize rainy images. Speci-cally, we build a full Bayesian generative model for rainy image where the rain layer is parameterized as a gen-erator with the input as some latent variables represent-ing the physical structural rain factors, e.g., direction, s-cale, and thickness. To solve this model, we employ the

Attentive Generative Adversarial Network for Raindrop Removal from a Single Image. rui1996DeRaindrop CVPR 2018 First, we propose a complementary cascaded network architecture, namely CCN, to remove rain streaks and raindrops in a unified framework. 1.