In various tasks of machine vision, image resolution is one of the important factors that affect the performance of the model. Generally, crop images with low resolution and lack of detail informa-tion may be collected. The picture is not good for the accuracy of yield prediction and crop pest identification. In this paper, tomato leaves are used as the target image, and the super-resolution reconstruction process takes advantage of the sharpness of the image, which has the characteristic of Scale invariance. Firstly, each image block is classified by using clustering algorithm accord-ing to the sharpness value of the image, and then wavelet transform is used to extract image features from each class of image blocks to get wavelet subbands respectively, subbands of each class not only train a union dictionary, but also learn a separate mapping function. Joint dictionary training and separate mapping matrix learning are helpful to optimize the high resolution and low resolution sparse coefficients. In the Reconstruction Stage: in order to reduce the image reconstruction time, the wavelet transform is only applied to the image blocks with a certain sharpness value, while the image reconstruction performance is basically unchanged, then the high-resolution image blocks are reconstructed by using the mapping function, coupled dictionary and the sparse representation coefficients of the image blocks. When the sharpness of the image block is lower than a certain sharpness value, the high and middle resolution image blocks will be superimposed to finally get the high resolution image.In the various tasks of machine vision, image resolution is one of the important factors that affect average PSNR value of the algorithm in this paper is 3.94 dB, 3.54 dB, 3.36 dB, 3.23 dB, 3.01 dB and 1.51 dB higher respectively.

Realization of Single Image Super-Resolution Reconstruction Based on Wavelet Transform and Coupled Dictionary

Villecco F.
2022

Abstract

In various tasks of machine vision, image resolution is one of the important factors that affect the performance of the model. Generally, crop images with low resolution and lack of detail informa-tion may be collected. The picture is not good for the accuracy of yield prediction and crop pest identification. In this paper, tomato leaves are used as the target image, and the super-resolution reconstruction process takes advantage of the sharpness of the image, which has the characteristic of Scale invariance. Firstly, each image block is classified by using clustering algorithm accord-ing to the sharpness value of the image, and then wavelet transform is used to extract image features from each class of image blocks to get wavelet subbands respectively, subbands of each class not only train a union dictionary, but also learn a separate mapping function. Joint dictionary training and separate mapping matrix learning are helpful to optimize the high resolution and low resolution sparse coefficients. In the Reconstruction Stage: in order to reduce the image reconstruction time, the wavelet transform is only applied to the image blocks with a certain sharpness value, while the image reconstruction performance is basically unchanged, then the high-resolution image blocks are reconstructed by using the mapping function, coupled dictionary and the sparse representation coefficients of the image blocks. When the sharpness of the image block is lower than a certain sharpness value, the high and middle resolution image blocks will be superimposed to finally get the high resolution image.In the various tasks of machine vision, image resolution is one of the important factors that affect average PSNR value of the algorithm in this paper is 3.94 dB, 3.54 dB, 3.36 dB, 3.23 dB, 3.01 dB and 1.51 dB higher respectively.
978-3-031-05229-3
978-3-031-05230-9
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4808897
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact