A Euler Video Color Enlargement Method Based on Deep Learning

Abstract

The invention discloses an Euler video color amplification method based on deep learning, which includes two steps of obtaining a color amplification network and calling a color amplification network. Firstly, a data set of simulated small color change pictures is synthesized, and a color amplification network constructed by four modules of spatial decomposition, differential filtering, amplification processing, and image reconstruction is trained on the data set; at runtime, the FIR bandpass filter is optimized and replaces the differential filtering module of the network. Step 2: When performing color amplification processing based on a given input video, the video is first decomposed into a frame sequence, and the color amplification frame sequence is generated by calling the color amplification network, and finally the color amplification video is synthesized. Compared with the linear amplification method, step 1 of the method of the present invention adopts a deep learning model, the training process is automated, and cumbersome manual design is eliminated; the processing of step 2 greatly reduces noise, does not generate artifacts in dynamic scenes, and enhances zoom effect.

Description

technical field [0001] The invention relates to the field of image and video processing, in particular to a deep learning-based Euler video color amplification method. Background technique [0002] Some color signals that exist objectively but cannot be directly observed by the naked eye contain rich information, such as periodic changes in skin color with blood circulation, local skin color changes caused by slight pressing, etc. The Euler video color amplification method has an excellent effect on enhancing the color changes that are difficult to perceive by the naked eye. It can capture and amplify the tiny color signals in the video, so that it can be directly observed by the human eye, providing a better way for interpreting such information. a visualization method. [0003] But there are some deficiencies in the methods that have been realized so far. First, the spatial decomposition process of the algorithm relies on manual design, and cannot separate the color sign...

AI Technical Summary

Problems solved by technology

First of all, the spatial decomposition process of the algorithm relies on manual design, and cannot separate the color signal from the motion signal, resulting in the amplification of the color change while the motion is also amplified, which will interfere with the color amplification result

Secondly, the existing time-domain filtering methods cannot take into account both static and dynamic scenes, and there are obvious artifacts in the amplification results in dynamic scenes

Third, the amplification results often have serious noise and are prone to distortion

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