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Found 7 papers, 2 papers with code
Multi-Weather Image Restoration via Histogram-Based Transformer Feature Enhancement
In this paper, we propose a Task Sequence Generator module that, in conjunction with the Task Intra-patch Block, effectively extracts task-specific features embedded in degraded images.
Multiple weather images restoration using the task transformer and adaptive mixup strategy
In this paper, we introduce a novel multi-task severe weather removal model that can effectively handle complex weather conditions in an adaptive manner.
Hierarchical Complementary Learning for Weakly Supervised Object Localization
The aim of removing parts from image or detected parts of the object is to force the model to detect the other features.
Entropy Guided Adversarial Model for Weakly Supervised Object Localization
Unfortunately, the network activates only the features that discriminate the object and does not activate the whole object.
Scalable Convolutional Neural Network for Image Compressed Sensing
Compared with the existing deep learning based image CS methods, SCSNet achieves scalable sampling and quality scalable reconstruction at any sampling ratio with only one model.
Deep Networks for Compressed Image Sensing
First of all, we train a sampling matrix via the network training instead of using a traditional manually designed one, which is much appropriate for our deep network based reconstruct process.
Single Image Super-Resolution with Dilated Convolution based Multi-Scale Information Learning Inception Module
With the novel dilated convolution based inception module, the proposed end-to-end single image super-resolution network can take advantage of multi-scale information to improve image super-resolution performance.
Ranked #78 on
Image Super-Resolution
on Set14 - 4x upscaling
