Recovering Realistic Texture in Image Super-resolution by Deep Spatial Feature Transform

Image super-resolution (SR) is the process of increasing the resolution of a low-resolution (LR) image to a higher resolution (HR) version. This is an important task in computer vision and has many practical applications, such as improving the quality of images captured by low-resolution cameras or enhancing the resolution of medical images. However, most existing SR methods suffer from a loss of texture details and produce overly smooth HR images, which can result in unrealistic and unappealing results.

To address this issue, a new SR method called Deep Spatial Feature Transform (DSFT) has been proposed. DSFT is a deep learning-based approach that uses a spatial feature transform layer to recover realistic texture in the HR image. The spatial feature transform layer takes the LR image and a set of HR feature maps as input and transforms the features to a higher dimensional space. This allows the model to better capture the high-frequency details in the image and produce more realistic HR images.

The DSFT method also employs a multi-scale approach, where the LR image is processed at multiple scales to capture both local and global features. Additionally, the model uses residual connections to improve the training process and reduce the risk of overfitting.

Experimental results show that DSFT outperforms state-of-the-art SR methods in terms of both quantitative metrics and visual quality. The method is also shown to be robust to different noise levels and image degradation scenarios.

In summary, DSFT is a promising approach for realistic texture recovery in image super-resolution. Its ability to capture high-frequency details and produce visually appealing HR images makes it a valuable tool for various applications in computer vision.