Learning Parallax Transformer Network for Stereo Image JPEG Artifacts Removal

Jiang, Xue-Yin; Tan, Weimin; Cheng, Ri; Zhou, Shili; Yan, Bo

doi:10.1145/3503161.3547986

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Cited by 5 publications

References 34 publications

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JPEG Quantized Coefficient Recovery via DCT Domain Spatial-Frequential Transformer

Ouyang,

Chen

2024

IEEE Trans. on Image Process.

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JPEG Quantized Coefficient Recovery via DCT Domain Spatial-Frequential Transformer

Ouyang,

Chen

2024

IEEE Trans. on Image Process.

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Stereo Image Restoration via Attention-Guided Correspondence Learning

Zhang,

Yu,

Jiang

et al. 2024

IEEE Trans. Pattern Anal. Mach. Intell.

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Residual Network for Image Compression Artifact Reduction

hu,

Luo,

Wang

et al. 2024

Int. J. Patt. Recogn. Artif. Intell.

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This paper proposes an image compression algorithm based on Swin Transformer and residual network (STRN), aiming to reduce blurring and distortions in traditionally compressed images. The algorithm utilizes a dual-channel mechanism to remove artifacts from the image, which takes advantage of the complementary features of the transform and residual networks. The Swin Transformer networks address the issue of long-range dependency, leading to an enhanced and improved reconstructed image quality. The residual network is an effective network that mitigates gradient loss and recovers image details during the image compression process. The paper demonstrates that image compression can be achieved by training a convolutional network based on a transformer and residuals network, which significantly reduces artifacts and provides better reconstructed image quality compared to previously used and current mainstream methods based on traditional convolutional neural networks. The proposed approach can remove blocking artifacts by subtracting estimated artifacts from the input image, while still preserving most of the original details. Therefore, our proposed method is highly effective in improving image quality and reducing visual artifacts caused by traditional compression methods. Moreover, this method is useful for enhancing image transmission and storage efficiency in various computer vision systems that employ digital visual codecs.

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Learning Parallax Transformer Network for Stereo Image JPEG Artifacts Removal

Cited by 5 publications

References 34 publications

JPEG Quantized Coefficient Recovery via DCT Domain Spatial-Frequential Transformer

JPEG Quantized Coefficient Recovery via DCT Domain Spatial-Frequential Transformer

Stereo Image Restoration via Attention-Guided Correspondence Learning

Residual Network for Image Compression Artifact Reduction

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