Single-image super-resolution (SISR) is an important task in image processing, which aims to enhance the resolution of imaging systems. Recently, SISR has made a huge leap and has achieved promising results with the help of deep learning (DL). In this ...

A new method is proposed to increase the accuracy of the state-of-the-art single image super-resolution (SISR) using novel training procedure. The proposed method, named post-trained convolutional neural network (CNN), is carried out stochastic ...

U-Net series models have achieved considerable success in various fields such as image segmentation and image classification. However, the decoders in these models often use transposed convolution (TC) from level to level, reducing the ...

With the use of convolutional neural networks, Single-Image Super-Resolution (SISR) has advanced dramatically in recent years. However, we notice a phenomenon that the structure of all these models must be consistent during training ...

Style transfer is the process that aims to recreate a given image (target image) with the style of another image (style image). In this work, a new style transfer scheme is proposed that uses a single-image super resolution (SISR) network to ...

Increasing model size often results in improved performance on super-resolution reconstruction. However, at some point large model cannot SR huge images due to GPU/TPU memory limitations. In this paper, to address this problem, we present Block-...

An efficient 2-step steganography technique is proposed to enhance stego image quality and secret message un-detectability. The first step is a pre-processing algorithm that reduces the size of secret images without losing information. This ...