Regression model-based fast single-image defogging algorithm and system

Abstract

The invention discloses a regression model-based fast single-image defogging algorithm and system. The algorithm includes a training process of a regression model and a processing process of a foggy image. The training process includes the following steps that: a fog-free image block is generated and is adopted as a sample; an atmospheric model is utilized to add fog to the sample; the sample feature value of the sample is extracted; and an SVM learning regression model is utilized according to the sample feature value. The processing process includes the following steps that: a foggy image is inputted, the foggy image is divided into a plurality of uniform blocks, and the maximum channel image of the foggy image is extracted; image block feature extraction is performed on the uniform blocks, and transmission parameters are estimated according to the SVM learning regression model, and guided filtering is adopted to optimize a transmission diagram; maximum value filtering and median filtering are performed on the extracted maximum channel image, guided filtering is adopted to optimize atmospheric light; and inverse transformation is carried out according to the filtering optimized transmission diagram and the optimized atmospheric light, so that a clear image can be obtained. With the algorithm adopted, fog removing processing can be performed on an image fast and accurately, and image processing quality can be effectively improved.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a fast single image defogging algorithm and system based on a regression model. Background technique [0002] Haze images are mostly taken in outdoor scenes under severe weather. Due to the existence of suspended particles in the air, the light source and scene reflections are scattered before entering the imaging device, resulting in brighter images, lower image contrast, saturation and other indicators, and signal-to-noise than increase. Such a change in the image will make the information difficult to read or lose appreciation for people. For subsequent image processing and computer vision algorithms, the possibility of algorithm failure is increased. Therefore, image defogging has a strong practical demand and development space. [0003] Early image defogging algorithms usually require other information besides the image, such as image depth informatio...

AI Technical Summary

Problems solved by technology

Tang et al. proposed a dehazing algorithm based on a learning model. This algorithm collected high-contrast image blocks as training samples, extracted multiple haze image-related features including dark channel features, and then used random forest to obtain a regression model. The algorithm The effect is very good, but it is necessary to take features of multiple scales for each pixel point, the time complexity is very high, and it is not suitable for occasions that require fast processing

Moreover, it needs to use different training sets for different defogging problems, which reduces the practicability of the algorithm.

[0007] However, in the current mainstream dehazing algorithm, there are inaccurate dehazing strength, slow calculation speed and cannot run in real time, and the dehazing effect on dense fog images is even less satisfactory

Images