Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network
In recent years, deep learning methods have been successfully applied to single-image super-resolution tasks. Despite their great performances, deep learning methods cannot be easily applied to real-world applications due to the requirement of heavy computation. In this paper, we address this issue by proposing an accurate and lightweight deep network for image super-resolution. In detail, we design an architecture that implements a cascading mechanism upon a residual network. We also present variant models of the proposed cascading residual network to further improve efficiency. Our extensive experiments show that even with much fewer parameters and operations, our models achieve performance comparable to that of state-of-the-art methods.
Code (3)
Tasks
Deep LearningImage Super-ResolutionSuper-ResolutionSimilar Papers 제목 키워드 기반
Multi-Level Feature Fusion Network for Lightweight Stereo Image Super-Resolution
Stereo image super-resolution utilizes the cross-view complementary information brought by the disparity effect of left and right perspective images to reconstruct higher-quality images. Cascading feature extraction modu…
Image Super-ResolutionStereo Image Super-ResolutionSuper-ResolutionLightweight high-resolution Subject Matting in the Real World
Existing saliency object detection (SOD) methods struggle to satisfy fast inference and accurate results simultaneously in high resolution scenes. They are limited by the quality of public datasets and efficient network …
Image Mattingobject-detectionObject DetectionAccurate Spectral Super-resolution from Single RGB Image Using Multi-scale CNN
Different from traditional hyperspectral super-resolution approaches that focus on improving the spatial resolution, spectral super-resolution aims at producing a high-resolution hyperspectral image from the RGB observat…
Spectral ReconstructionSpectral Super-ResolutionSuper-ResolutionEBSR: Feature Enhanced Burst Super-Resolution With Deformable Alignment
We propose a novel architecture to handle the problem of multi-frame super-resolution (MFSR). The proposed framework is known as Enhanced Burst Super-Resolution (EBSR), which divides the MFSR problem into three parts: al…
Burst Image ReconstructionBurst Image Super-ResolutionMulti-Frame Super-ResolutionSuper-ResolutionDIPNet: Efficiency Distillation and Iterative Pruning for Image Super-Resolution
Efficient deep learning-based approaches have achieved remarkable performance in single image super-resolution. However, recent studies on efficient super-resolution have mainly focused on reducing the number of paramete…
Image Super-ResolutionNetwork PruningSuper-Resolution