Real image denoising method based on multi-scale fusion and edge enhancement

Abstract

The invention discloses a real image denoising method based on multi-scale fusion and edge enhancement, which belongs to the technical field of computer vision images. In the image input stage, in order to improve the generalization ability of the model, data enhancement is designed, and some pixels of the content of the input noise image are randomly selected to replace the corresponding noise-free image; using three convolution kernels with different receptive field sizes, the input Multi-level smoothing processing is performed on the noise image to obtain preliminary smoothing results of three different scales; the channel attention mechanism is used to adaptively express the multi-scale denoising results, and then fused; the edge is extracted by the Laplacian operator, and the introduction The edge and texture information of the original noise image can be used to enhance the details of the fused smooth image to improve the visual effect; the design of the invention is reasonable, and while achieving a better denoising effect, it maintains a faster running speed, and the overall effect is real It has achieved good results in image denoising.

Description

technical field [0001] The invention relates to the technical field of computer vision images, in particular to a real image denoising method based on multi-scale fusion and edge enhancement. Background technique [0002] With the advancement of science and technology, mobile devices are becoming more and more popular, and image acquisition is becoming more and more convenient. Due to the use of relatively low-cost sensors and lenses, the images captured by mobile cameras such as mobile phone cameras are usually disturbed by noise, especially when the light is insufficient, the impact of noise is more serious, which will lead to a decrease in image quality. bring difficulty. Ensuring image quality is the basis for high-level vision applications such as object detection and semantic segmentation on images. Therefore, how to efficiently denoise real images and improve image quality is an important research topic in the field of computer vision. [0003] Real-Image Denoising...

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to solve the problem that existing image denoising methods fail to pay attention to the variety and complexity of real noise, do not consider the different importance between feature channels, and fail to fully utilize multi-scale features. Therefore, the problem of relatively limited effects has been achieved, and a real image denoising method based on multi-scale fusion and edge enhancement is proposed that is reasonably designed, fully considers multi-scale information to improve the noise removal effect, and is relatively lightweight.

Images