5/2/2023 0 Comments Chroma fang![]() Deep convolution network often contains more convolution layer processing, and the algorithm efficiency is generally low. When there were objects with similar structure and direction of raindrops in the image, it was difficult for these algorithms to keep the original structure information while eliminating raindrops.ĭeep learning, also known as deep convolution neural network, has a deep network structure, which can express complex deep image features, and has achieved very good results in image recognition, image rain removal, image restoration and other fields. Although the effect of these algorithms was improved to some extent, the effect of rain removal was still limited due to the use of only shallow image features. proposed a discriminative sparse coding algorithm with classification ability, and used it to improve the accuracy of raindrop detection. However, in the process of sparse representation to restore the high-frequency part of the image, there was information loss, which was easy to cause the blur of the local texture of the repaired image. ![]() The algorithm used the advantages of sparse representation in image denoising, built a complete dictionary library of high frequency components of image through dictionary learning, and then reconstructed rainless image through sparse coding. proposed a rain removal algorithm for single image based on morphological component analysis. Experimental results show that compared with other algorithms, the proposed algorithm achieves the highest value in both peak signal-to-noise ratio (PSNR) and structural similarity, which shows that the image effect of the algorithm is better after rain removal.Ī large number of rain removal algorithms have been proposed successively, among which the typical rain removal algorithm for single image is to treat the raindrop in the image as a special high-frequency noise, and then use the algorithm of image decomposition or raindrop recognition to filter the raindrop. After training the network, the optimal parameters of the network are obtained, and finally the convolution neural network which can effectively remove the rain line is obtained. ![]() The residual image and brightness component are overlapped again, the reconstructed image is restored to RGB space by YUV inverse transformation, and the final color raindrop free image is obtained. the brightness component and residual component of the raindrop source image and the ideal recovered image without raindrop are extracted. The image is denoised by using wavelet decomposition, threshold value and wavelet reconstruction in wavelet transform, and the rain drop image is transformed from RGB space to YUV (luma chroma) space by using deep learning to obtain the brightness component and color component of the image. Because of the important characteristics of wavelet transform, such as symmetry, orthogonality, flexibility and limited support, wavelet transform is used to remove rain from a single image. To solve this problem, a single image rain removal algorithm based on deep learning and symmetric transformation is proposed. Rainy, as an inevitable weather condition, will affect the acquired image.
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