ABSTRACT
The rain removal from an image in the rainy season is also a required task to identify the object in it. It is a challenging problem and has been recently investigate extensively. In this project the entropy maximization and background estimation based method is used for the rain removal. This method is based on single-image rain removal framework. The raindrops are greatly differing from the background, as the intensity of rain drops is higher the background. The entropy maximization is very much suitable for the rain removal. Experimental results express the efficacy of the rain removal by proposed algorithm is better than the method based on saturation and visibility features. The rain and non-rain parts in a single image are very closely mixed up and the identification of rain streaks is not an easy task. In this project, we compare a single-image rain streak removal based on morphological component analysis (MCA) by decomposition of rain streaks. The signal and image processing for the filtering and region specification are discussed in the previous works. In this method, a bilateral filter is applied for an image to decompose it into the low-frequency (LF) and high-frequency (HF) parts. The HF part is then decomposed into rain component and non-rain component by performing sparse coding and dictionary learning on MCA.
PROJECT OUTPUT
PROJECT VIDEO
Contact:
Mr. Roshan P. Helonde
Mobile: +91-7276355704
WhatsApp: +91-7276355704
Email: roshanphelonde@rediffmail.com
.png)
.png)
