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Found 10 papers, 1 papers with code
Sem-CS: Semantic CLIPStyler for Text-Based Image Style Transfer
However, the ground semantics of objects in the style transfer output is lost due to style spill-over on salient and background objects (content mismatch) or over-stylization.
SEM-CS: Semantic CLIPStyler for Text-Based Image Style Transfer
Sem-CS first segments the content image into salient and non-salient objects and then transfers artistic style based on a given style text description.
ContextCLIP: Contextual Alignment of Image-Text pairs on CLIP visual representations
However, it showed negative transfer performance on standard datasets, e. g., BirdsNAP, RESISC45, and MNIST.
FMD-cGAN: Fast Motion Deblurring using Conditional Generative Adversarial Networks
With the help of MobileNet based architecture that consists of depthwise separable convolution, we reduce the model size and inference time, without losing the quality of the images.
DeepObjStyle: Deep Object-based Photo Style Transfer
DeepObjStyle preserves the semantics of the objects and achieves better style transfer in the challenging scenario when the style and the content images have a mismatch of image features.
DILIE: Deep Internal Learning for Image Enhancement
Recent methods for image enhancement consider the problem by performing style transfer and image restoration.
DeepCFL: Deep Contextual Features Learning from a Single Image
In this work, we proposed a new training data-independent framework, called Deep Contextual Features Learning (DeepCFL), to perform image synthesis and image restoration based on the semantics of the input image.
Blind Motion Deblurring through SinGAN Architecture
SinGAN is a generative model that is unconditional and could be learned from a single natural image.
DCIL: Deep Contextual Internal Learning for Image Restoration and Image Retargeting
Recently, there is a vast interest in developing methods which are independent of the training samples such as deep image prior, zero-shot learning, and internal learning.
Multi-level Encoder-Decoder Architectures for Image Restoration
In this paper, we propose a framework based on the multi-level extensions of the encoder-decoder network, to investigate interesting aspects of the relationship between image restoration and network construction independent of learning.
