Correction of visual artifacts caused by JPEG compression, these artifacts are usually grouped into three types: blocking, blurring, and ringing. They are caused by quantization and removal of high frequency DCT coefficients.
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Discriminative model learning for image denoising has been recently attracting considerable attentions due to its favorable denoising performance.
In this work, we propose a very deep fully convolutional auto-encoder network for image restoration, which is a encoding-decoding framework with symmetric convolutional-deconvolutional layers.
Ranked #2 on Grayscale Image Denoising on BSD200 sigma10
We fully exploit the hierarchical features from all the convolutional layers.
We apply MemNet to three image restoration tasks, i. e., image denosing, super-resolution and JPEG deblocking.
With the modified U-Net architecture, wavelet transform is introduced to reduce the size of feature maps in the contracting subnetwork.
Ranked #1 on Grayscale Image Denoising on BSD68 sigma25
Lossy compression introduces complex compression artifacts, particularly the blocking artifacts, ringing effects and blurring.
Ranked #3 on JPEG Artifact Correction on ICB (Quality 30 Color)