Yuwen Li

Chaobing Zheng, Yuwen Li, Shiqian Wu

It is challenging to remove rain-steaks from a single rainy image because the rain steaks are spatially varying in the rainy image. This problem is studied in this paper by combining conventional image processing techniques and deep learning based techniques. An improved weighted guided image filter (iWGIF) is proposed to extract high frequency information from a rainy image. The high frequency information mainly includes rain steaks and noise, and it can guide the rain steaks aware deep convolutional neural network (RSADCNN) to pay more attention to rain steaks. The efficiency and explain-ability of RSADNN are improved. Experiments show that the proposed algorithm significantly outperforms state-of-the-art methods on both synthetic and real-world images in terms of both qualitative and quantitative measures. It is useful for autonomous navigation in raining conditions.

Yuwen Li, Zhengguo Li, Chaobing Zheng et al.

Existing shape from focus (SFF) techniques cannot preserve depth edges and fine structural details from a sequence of multi-focus images. Moreover, noise in the sequence of multi-focus images affects the accuracy of the depth map. In this paper, a novel depth enhancement algorithm for the SFF based on an adaptive weighted guided image filtering (AWGIF) is proposed to address the above issues. The AWGIF is applied to decompose an initial depth map which is estimated by the traditional SFF into a base layer and a detail layer. In order to preserve the edges accurately in the refined depth map, the guidance image is constructed from the multi-focus image sequence, and the coefficient of the AWGIF is utilized to suppress the noise while enhancing the fine depth details. Experiments on real and synthetic objects demonstrate the superiority of the proposed algorithm in terms of anti-noise, and the ability to preserve depth edges and fine structural details compared to existing methods.

Yuwen Li, Chaobing Zheng, Shiqian Wu et al.

Aiming at the existing single image haze removal algorithms, which are based on prior knowledge and assumptions, subject to many limitations in practical applications, and could suffer from noise and halo amplification. An end-to-end system is proposed in this paper to reduce defects by combining the prior knowledge and deep learning method. The haze image is decomposed into the base layer and detail layers through a weighted guided image filter (WGIF) firstly, and the airlight is estimated from the base layer. Then, the base layer image is passed to the efficient deep convolutional network for estimating the transmission map. To restore object close to the camera completely without amplifying noise in sky or heavily hazy scene, an adaptive strategy is proposed based on the value of the transmission map. If the transmission map of a pixel is small, the base layer of the haze image is used to recover a haze-free image via atmospheric scattering model, finally. Otherwise, the haze image is used. Experiments show that the proposed method achieves superior performance over existing methods.