Extensive experiments demonstrate that owing to the informativeness of the camera raw data, the effectiveness of the network architecture, and the separation of super-resolution and color correction processes, the proposed method achieves superior VSR results compared to the state-of-the-art and can be adapted to any specific camera-ISP. The reconstruction module, built with elaborately designed Attention-based Residual Dense Blocks (ARDBs), serves the purpose of 1) refining the fused feature and 2) learning the color information needed to generate a spatial-specific transformation for accurate color correction. While significant progress has been made in single image super-resolution, most algorithms only perform well on synthetic data, which limits.

The SDI module is designed according to the architectural principle suggested by a canonical decomposition result for Hidden Markov Model (HMM) inference it estimates the target high-resolution frame by repeatedly performing pairwise feature fusion using deformable convolutions. title 'Exploiting Raw Images for Real-Scene Super-Resolution', abstract 'Super-resolution is a fundamental problem in computer vision which aims to overcome the spatial limitation of camera sensors. Let me know what you think about this new feature in Adobe Camera Raw Tutorial Asset. This method consists of a Successive Deep Inference (SDI) module and a reconstruction module, among others. Super Resolution uses an advanced machine learning model to intelligently double the resolution of an image while maintaining clean edges and preserving important details. To address this issue, we propose a new VSR method that can directly exploit camera sensor data, accompanied by a carefully built Raw Video Dataset (RawVD) for training, validation, and testing. Such methods are 1) inherently suboptimal due to information loss incurred by non-invertible operations in ISP, and 2) inconsistent with the real imaging pipeline where VSR in fact serves as a pre-processing unit of ISP. To the best of our knowledge, the existing deep-learning-based Video Super-Resolution (VSR) methods exclusively make use of videos produced by the Image Signal Processor (ISP) of the camera system as inputs.

Camera raw super resolution