Binocular super-resolution image detection method and system based on cavity convolution and feature fusion, and medium

Abstract

The invention discloses a binocular super-resolution image detection method and system based on cavity convolution and feature fusion and a medium, and the method comprises the following steps: inputting a binocular image group into a classical binocular image super-resolution network, generating a binocular super-resolution image as a negative sample set, and taking an original binocular image group as a positive sample set; cutting the positive and negative sample data sets into image blocks and randomly dividing training set image blocks and test set image blocks; preprocessing the image blocks, converting the image blocks into grayscale images, and filtering the grayscale images by using a high-pass filter to obtain filtered images; constructing a binocular super-resolution image detection network, and inputting the training set filtering image for training to obtain a trained network; and inputting a test set filtering image into the trained network, and outputting a category corresponding to the classification with the maximum probability to obtain an image detection result. The method directly detects the input image, is suitable for detection of images of various sizes, has good detection performance, is short in detection time, and can realize real-time detection.

Description

technical field [0001] The invention relates to the technical field of digital image forensics, in particular to a binocular super-resolution image detection method, system and medium based on atrous convolution and feature fusion. Background technique [0002] In recent years, with the development and application of computer vision and computational photography technologies, the imaging capabilities of smartphones have undergone tremendous changes. The multi-camera module replaces the single-camera module, and the multi-camera ISP algorithm with functions such as multi-frame fusion that adapts to the multi-camera module replaces the standard ISP algorithm of digital cameras. New imaging equipment and ISP algorithms, especially the process of fusing multiple images into a single image, may lead to failure and misjudgment of traditional forensics algorithms, including device traceability and tampering forensics. [0003] Multi-eye image super-resolution is a common function ...

AI Technical Summary

Problems solved by technology

[0004] The main purpose of the present invention is to overcome the shortcomings and deficiencies of the prior art, and provide a binocular super-resolution image detection method based on atrous convolution and feature fusion. Defects with poor performance and weak robustness ensure the accuracy of detection and realize efficient and real-time detection performance

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