Super-resolution image retrieval system based on deep hash

An image retrieval and super-resolution technology, which is applied in the field of super-resolution image retrieval systems, can solve the problems of large differences in hash codes and the inability to extract image features more accurately, and achieve the effect of improving resolution and retrieval accuracy

Pending Publication Date: 2022-04-01
河南垂天科技有限公司
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Problems solved by technology

[0005] The present invention provides a super-resolution image retrieval system based on deep hashing, aiming to solve the problem that the hash code of the query image of the image retriev

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[0024] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention, but not to limit the present invention.

[0025] see Figure 1-2 , the present invention provides a technical solution: a deep hash-based super-resolution image retrieval system, comprising:

[0026] S1. Perform super-resolution reconstruction processing on the query image (a) and the similar image (a+), that is, input the image into the VDSR layer, and the reconstruction process is as follows:

[0027] (1) The input original image is represented by LR, and bicubic linear interpolation is performed on the image to obtain the up-sampled image UR;

[0028] (2), input UR into a 20-layer convolutional neural network to obtain high-f...

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Abstract

The invention is applicable to the technical field of image retrieval systems, and provides a deep hash-based super-resolution image retrieval system, which comprises the following steps of: S1, performing super-resolution reconstruction processing on a to-be-queried image (a) and a similar image (a +), namely inputting the images into a VDSR layer; and S2, inputting images SR (a), SR (a +) and a-obtained by reconstruction of a and a + into a CNN layer to extract corresponding image features. According to the super-resolution image retrieval system based on deep Hash, before the image is input into the neural network for feature extraction, super-resolution reconstruction is firstly carried out on the image, the resolution of the image is improved to obtain more detailed features, then the more detailed features can be extracted, the retrieval precision is improved, and when back propagation is carried out, the retrieval efficiency is improved. Compared with automatic learning updating of hyper-parameters and weights of low-resolution images, the high-resolution images are more reasonable, and the network obtained through training in the mode can obtain a better result compared with an original network even if the low-resolution images are input.

Description

technical field [0001] The invention belongs to the technical field of image retrieval systems, in particular to a deep hash-based super-resolution image retrieval system. Background technique [0002] As the convolutional neural network shines in the field of images, image retrieval technology is becoming more and more mature. Image retrieval is a crucial technology in the fields of medical images and automatic driving. Therefore, how to improve the accuracy of image retrieval has become an important issue. Research hotspots at home and abroad. [0003] Image retrieval technology uses text descriptions to describe the characteristics of images, such as the author, age, genre, and size of paintings. Image retrieval technology that analyzes and retrieves image content semantics such as image color, texture, layout, etc., that is, content-based image retrieval technology. CBIR is a type of content-based retrieval (CBR for short). CBR also includes dynamic video , audio and o...

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Application Information

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IPC IPC(8): G06F16/51G06F16/53G06F16/583G06N3/04
Inventor 倪水平朱明甫洪振东张海洋赵波李朋坤金旭朱智丹
Owner 河南垂天科技有限公司
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