JPEG Artifact Removal
10 papers with code • 0 benchmarks • 0 datasets
Benchmarks
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Latest papers with no code
Lightweight Adaptive Feature De-drifting for Compressed Image Classification
However, it is not an ideal choice to use these JPEG artifact removal methods as a pre-processing for compressed image classification for the following reasons: 1.
JPEG Quantized Coefficient Recovery via DCT Domain Spatial-Frequential Transformer
However, the current DCT domain methods typically suffer from limited effectiveness in handling a wide range of compression quality factors, or fall short in recovering sparse quantized coefficients and the components across different colorspace.
Training a Task-Specific Image Reconstruction Loss
The choice of a loss function is an important factor when training neural networks for image restoration problems, such as single image super resolution.
Residual learning based densely connected deep dilated network for joint deblocking and super resolution
However, training deeper networks is very challenging because of the problem of vanishing gradients.
DMCNN: Dual-Domain Multi-Scale Convolutional Neural Network for Compression Artifacts Removal
JPEG is one of the most commonly used standards among lossy image compression methods.
