Quick defogging processing assembly for fog images

Abstract

The invention discloses a quick defogging processing assembly for fog images. The quick defogging processing assembly for the fog images comprises a video input checking module, a video input statistic module, a video processing module, a video output module and an external storage controlling module, wherein the video processing module is the core of the quick defogging processing assembly and comprises a first-frame removing unit, a frame frequency controlling unit, a pixel minimum extracting unit, a local minimum extracting unit, an image edge expanding unit, a row filtering unit, parallel column filtering units and an enhancement processing unit. By utilizing a quick Gaussian convolution algorithm, the quick defogging processing assembly for the fog images is simple in structure and algorithm, low in realization difficulty and capable of greatly saving sources and reducing delays; by utilizing the multiple parallel column filtering units for row and column filtering of video streaming, the quick defogging processing assembly for the fog images reads data by rows during processing and then skips by columns, and can acquire multiple columns of data simultaneously and input the same into multiple Gaussian row filtering circuits for parallel processing synchronously, so that lost time for skipping by column addresses is compensated, and instantaneity for defogging processing is guaranteed.

Description

technical field [0001] The invention relates to a haze image fast defogging processing component. Background technique [0002] In the field of computer vision, the sharpening of images in foggy scenes is an important issue, which is crucial for subsequent operations such as visual interpretation and computer vision analysis. In the range of visible light imaging, due to the influence of fog, dust and other particles in the atmosphere, as the transmission distance increases, the light reflected by the object and reaching the photosensitive sheet of the camera is very weak, making the image blurred, especially in foggy weather. Visibility is very low, and the haze of images taken in foggy weather is even more serious. Therefore, it is necessary to restore the weak details of the image through defogging methods. Image defogging technology is especially important for the construction of a safe city and has great practical significance: image defogging technology can be applied...

AI Technical Summary

Problems solved by technology

[0008] (2) The color of objects or various surfaces: For example, many objects (such as green grass, trees and various plants, red or yellow flowers and leaves, blue sea surface, etc.) have only a single color, The lack of other colors of the RGB three-channel color will result in low brightness of the dark channel

[0009] (3) Dark objects or surfaces: For example, darker tree trunks and stones, because the outdoor natural scene images are full of shadows and various colors, the dark channel brightness of these images is low

In the traditional smog image defogging processing system, when the video stream is processed after the statistics are completed, the frame rate of the video stream is directly controlled. Incomplete, therefore, the data of the first frame will usually be blocked, and incomplete video data will cause storage and display errors, which will directly affect the effect of video frame rate control, thereby affecting the extraction of the minimum pixel value and ultimately affecting smog image defogging effect

In addition, in the traditional haze image defogging processing system, the processing algorithm for the regional dark channel image uses filters such as Soft Matting Filter and Guided Filter to obtain the transfer function of the simulated fog scene. This method has a better defogging effect , but due to the complexity of the algorithm, it is not easy for engineering implementation and real-time processing

The advantage of using Gaussian filtering to process regional dark channel images is that the structure is simple, but its disadvantage is that the template is usually large, and direct calculation will consume a lot of resources and time, making it difficult to implement.

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