Advanced driver assistance system (ADAS) can recognize traffic signals, vehicles, pedestrians, and so on all over the vehicle. However, because the ADAS is based on images taken in an outdoor environment, it is susceptible to ambient weather such as fog. So, preprocessing such as de-fog and de-hazing techniques is required to prevent degradation of object recognition performance due to decreased visibility. But, if such a fog removal technique is applied in an environment where there is little or no fog, the visual quality may be deteriorated due to excessive contrast improvement. And in foggy road environments, typical fog removal algorithms suffer from color distortion. In this paper, we propose a temporal filter-based fog detection algorithm to selectively apply de-fogging method only in the presence of fog. We also propose a method to avoid color distortion by detecting the sky region and applying different methods to the sky region and the non-sky region. Experimental results show that in the actual images, the proposed algorithm shows an average of more than 97% fog detection accuracy, and improves subjective image quality of existing de-fogging algorithms. In addition, the proposed algorithm shows very fast computation time of less than 0.1ms per frame.

This letter presents an automatic 2D-to-3D conversion method using a structure from motion (SfM) process for multi-object scenes. The foreground and background regions may have different depth values in an image. First, we detect the foreground objects and the background by using a depth histogram. Then, the proposed method creates the virtual image by projecting each region with its computed projective matrix. Experimental results compared to previous research show that the proposed method provides realistic stereoscopic images.

This paper presents a single image super-resolution (SR) algorithm based on self-similarity using non-local-mean (NLM) metric. In order to accurately find the best self-example even under noisy environment, NLM weight is employed as a self-similarity metric. Also, a pixel-wise soft-switching is presented to overcome an inherent drawback of conventional self-example-based SR that it seldom works for texture areas. For the pixel-wise soft-switching, an edge-oriented saliency map is generated for each input image. Here, we derived the saliency map which can be robust against noises by using a specific training. The proposed algorithm works as follows: First, auxiliary images for an input low-resolution (LR) image are generated. Second, self-examples for each LR patch are found from the auxiliary images on a block basis, and the best match in terms of self-similarity is found as the best self-example. Third, a preliminary high-resolution (HR) image is synthesized using all the self-examples. Next, an edge map and a saliency map are generated from the input LR image, and pixel-wise weights for soft-switching of the next step are computed from those maps. Finally, a super-resolved HR image is produced by soft-switching between the preliminary HR image for edges and a linearly interpolated image for non-edges. Experimental results show that the proposed algorithm outperforms state-of-the-art SR algorithms qualitatively and quantitatively.

In this paper, we propose a filtering approach based on global motion estimation (GME) and global motion compensation (GMC) for pre- and postprocessing of video codecs. For preprocessing a video codec, group of pictures (GOP), which is a basic unit for GMC, and reference frames are first defined for an input video sequence. Next, GME and GMC are sequentially performed for every frame in each GOP. Finally, a block-based adaptive temporal filter is applied between the GMC frames before video encoding. For postprocessing a video codec at the decoder end, every decoded frame is inversely motion-compensated using the transmitted global motion information. The holes generated during inverse motion compensation can be filled with the reference frames. The experimental results show that the proposed algorithm provides higher Bjontegaard-delta peak signal-to-noise ratios (BD-PSNRs) of 0.63 and 0.57 dB on an average compared with conventional H.264 and HEVC platforms, respectively.