Industrial Technology Research Institute

De-Qin Gao

Peter Chondro

Shanq-Jang Ruan

Mei-En Shao

The disclosure is directed to an image dehazing method and an image dehazing apparatus using the same method. In an aspect, the disclosure is directed to an image dehazing method, and the method would include not limited to: receiving an input image; dehazing the image by a dehazing module to output a dehazed RGB image; recovering image brightness of the dehazed RGB image by a high dynamic range (HDR) module to output an HDR image; and removing reflection of the HDR image by a ReflectNet inference model, wherein the ReflectNet inference model uses a deep learning architecture.

Image dehazing method and image dehazing apparatus using the same

An image recognition method, including: obtaining an image to be recognized by an image sensor; inputting the image to be recognized to a single convolutional neural network; obtaining a first feature map of a first detection task and a second feature map of a second detection task according to an output result of the single convolutional neural network, wherein the first feature map and the second feature map have a shared feature; using an end-layer network module to generate a first recognition result corresponding to the first detection task from the image to be recognized according to the first feature map, and to generate a second recognition result corresponding to the second detection task from the image to be recognized according to the second feature map; and outputting the first recognition result corresponding to the first detection task and the second recognition result corresponding to the second detection task.

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Yunlin County, Taiwan

De-Qin Gao