A noise reduction convolutional auto-encoding device, comprising: a processor; and a storage device, wherein the processor is configured to access a noise reduction convolutional auto-encoding model stored in the storage device to execute the noise reduction convolutional auto-encoding model, wherein the processor executes: receiving a distorted image, and inputting the distorted image into the noise reduction convolutional auto-encoding model; in the noise reduction convolutional auto-encoding model, an image feature of the distorted image is transferred to a first deconvolution layer through skip-connection; characterized in that the processor further executes: performing a plurality of multi-stride encoding convolutional layers (Ll2, Ll4, Ll6, Ll7) for the distortion image to reduce a dimension, and then performing a same-dimensional encoding convolutional layer (L13, Ll5); according to the corresponding multi-stride encoding convolutional layers and same-dimensional encoding convolutional layers, a plurality of decoding multi-stride convolutional layers (Lu1, Lu3, Lu5, Lu7) and a same-dimensional decoding convolutional layers (Lu2, Lu4, Lu6) are upgraded; and inputting a result of up-scaled dimension to the same-dimensional decoding convolutional layer of a balanced channel using the first deconvolution layer, and outputting a reconstructed image via the same-dimensional decoding convolutional layer of the balanced channel. a processor; and a storage device, wherein the processor is configured to access a noise reduction convolutional auto-encoding model stored in the storage device to execute the noise reduction convolutional auto-encoding model, wherein the processor executes: receiving a distorted image, and inputting the distorted image into the noise reduction convolutional auto-encoding model; in the noise reduction convolutional auto-encoding model, an image feature of the distorted image is transferred to a first deconvolution layer through skip-connection; characterized in that the processor further executes: performing a plurality of multi-stride encoding convolutional layers (Ll2, Ll4, Ll6, Ll7) for the distortion image to reduce a dimension, and then performing a same-dimensional encoding convolutional layer (L13, Ll5); according to the corresponding multi-stride encoding convolutional layers and same-dimensional encoding convolutional layers, a plurality of decoding multi-stride convolutional layers (Lu1, Lu3, Lu5, Lu7) and a same-dimensional decoding convolutional layers (Lu2, Lu4, Lu6) are upgraded; and inputting a result of up-scaled dimension to the same-dimensional decoding convolutional layer of a balanced channel using the first deconvolution layer, and outputting a reconstructed image via the same-dimensional decoding convolutional layer of the balanced channel. receiving a distorted image, and inputting the distorted image into the noise reduction convolutional auto-encoding model; in the noise reduction convolutional auto-encoding model, an image feature of the distorted image is transferred to a first deconvolution layer through skip-connection; characterized in that the processor further executes: performing a plurality of multi-stride encoding convolutional layers (Ll2, Ll4, Ll6, Ll7) for the distortion image to reduce a dimension, and then performing a same-dimensional encoding convolutional layer (L13, Ll5); according to the corresponding multi-stride encoding convolutional layers and same-dimensional encoding convolutional layers, a plurality of decoding multi-stride convolutional layers (Lu1, Lu3, Lu5, Lu7) and a same-dimensional decoding convolutional layers (Lu2, Lu4, Lu6) are upgraded; and inputting a result of up-scaled dimension to the same-dimensional decoding convolutional layer of a balanced channel using the first deconvolution layer, and outputting a reconstructed image via the same-dimensional decoding convolutional layer of the balanced channel. performing a plurality of multi-stride encoding convolutional layers (Ll2, Ll4, Ll6, Ll7) for the distortion image to reduce a dimension, and then performing a same-dimensional encoding convolutional layer (L13, Ll5); according to the corresponding multi-stride encoding convolutional layers and same-dimensional encoding convolutional layers, a plurality of decoding multi-stride convolutional layers (Lu1, Lu3, Lu5, Lu7) and a same-dimensional decoding convolutional layers (Lu2, Lu4, Lu6) are upgraded; and inputting a result of up-scaled dimension to the same-dimensional decoding convolutional layer of a balanced channel using the first deconvolution layer, and outputting a reconstructed image via the same-dimensional decoding convolutional layer of the balanced channel.