Zhejiang Gongshang University

Zhejiang Dahua Technology Co., Ltd.

Weigang Chen

Xingming Zhang

Yinchang Yang

Weizhong Yao

Yanbin Liu

Jingang Lu

A lane detection method integratedly using image enhancement and a deep convolutional neural network. On the assumption that lanes have similar widths in a local region of an image and a lane can be segmented into several image blocks, each of which contains lane marking in the center, a method based on a deep convolutional neural network is provided to detect lane marking blocks in the image. Input to the model includes road images captured by a camera as well as a set of enhanced images generated by the contrast limited adaptive histogram equalization (CLAHE) algorithm. The method according to the present disclosure can effectively overcome difficulties of lane detection under complex imaging conditions, such as poor image quality, and small lane marking targets, so as to achieve better robustness.

Lane detection method integratedly using image enhancement and deep convolutional neural network

The disclosure discloses a method, a medium, and a device for enhancing event camera image reconstruction by fusing visible images, and belongs to the field of image processing. In order to solve the problem that the reconstructed image of the event camera is likely affected by noise and lacks of photorealistic, the method provided by the disclosure adopts an enhancement module based on the improved Transformer model to fuse the intensity image reconstructed from the event stream and the reference image selected from the visible image sequence captured by an optical sensor, so that makes the reconstructed image has an appearance close to the visible image while maintaining the characteristics of high dynamic range of the event camera.

Method, medium and device for enhancing event camera image reconstruction by fusing visible images

A method relating to image compression includes acquiring image data of an image, wherein the image includes a matrix of pixels, and the image data include pixel values of the matrix of pixels. The method also includes generating, based on the image data, a first-level data set including pixel values of a portion of the matrix of pixels. The method further includes determining at least one coding mode for the first-level data set, encoding the first-level data set into a code stream based on the at least one coding mode, and packaging the code stream into a modified code stream.

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Hangzhou, China

Weigang Chen