Image super-resolution reconstruction method based on deep learning iteration up-down sampling
A super-resolution reconstruction and deep learning technology, applied in the field of image super-resolution reconstruction based on deep learning iterative upsampling and downsampling, can solve the problems of difficult application of magnification and inability to express high-level features, etc., to improve sensitivity, The effect of increasing the receptive field and improving performance
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[0037] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in combination with specific embodiments and with reference to the accompanying drawings. It should be understood that these descriptions are exemplary only, and are not intended to limit the scope of the present invention. Also, in the following description, descriptions of well-known structures and techniques are omitted to avoid unnecessarily obscuring the concept of the present invention.
[0038] A detailed description will be given below in conjunction with the accompanying drawings.
[0039] Based on the deep learning theory, the present invention constructs an end-to-end network, which is based on iterative up-down sampling, adding a hole convolution network to extract the context information in the image, and combining all the convolution layers in the up-down sampling Use hole convolution, that is, the ...
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