Wen Jun Li

Bio: Wen Jun Li is an academic researcher from Hangzhou Dianzi University. The author has contributed to research in topics: Deep learning & Image (mathematics). The author has an hindex of 1, co-authored 1 publications receiving 1 citations.

Deep sea image enhancement algorithm based on distance model

Abstract: In the process of deep sea image acquisition, the illumination of the image is uneven due to the limitation of the deep sea imaging conditions. In this case, an image enhancement algorithm for auto adaptive exposure is proposed. First the algorithm calculates the distance between the light source and the imaging object based on the previous and the next frame, then calculate the exposure coefficient based on the distance, and then adaptively expose the image based on the exposure coefficient to achieve the purpose of enhancing the image. The experimental results show that this algorithm can effectively restore the uneven of the illumination due to the submarine conditions.

Removing rain from images via combining detection and removal

Abstract: The performance of rain removal methods which are based on deep learning is largely affected by the designed models and training datasets for the image rain removal tasks. Most of current state-of-the-art focus on how to construct powerful deep models. But in this paper, we start from two perspectives of training dataset and model. We propose a novel rain model that includes a rain layer, a background layer and and a way how rainy image is generated. Based on this model, we develop a multi-task deep learning architecture that learns features of both the rain layer and the clean background layer. The additional information of rain layer is important because its loss function can provide additional powerful information to the network. Then we collected a large number of images of real rain streaks and outdoor scenes, and produced datasets for training. The effectiveness of our model and architecture was shown in tests on synthetic datasets.

A Novel Method of Image Enhancement Based on Mapping Threshold

Abstract: Aiming at the difficulty in determining the mapping range in gray-level transformation,a novel image enhancement method based on mapping threshold is proposed. The lowest point between target area and background area is searched automatically in the mapping transformation of histogram gray-level. Then it is operated as a threshold for mapping of image gray-level scale so as to adjust the gray coverage of the image. Finally,the brightness and contrast of the image are improved. Experiment analysis demonstrates that,the proposed method can adjust the image dynamic gray-level range so that obtain better enhancement effects than conventional gray histogram transformation. The new method has solved the problem of determining the image gray-level scale in the mapping transformation,is an effective image enhancement method.