How to get an accurate image of multi-class targets scene is the key to synthetic aperture radar (SAR) imaging research. In sparse SAR imaging, compound regularization constrains multi-class target features by setting...
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End-to-end image coding methods based on wavelet-like transform have made great progress in recent years. The most advanced one is iWave++, which adopts multi-level lifting schemes based on convolutional neural networ...
End-to-end image coding methods based on wavelet-like transform have made great progress in recent years. The most advanced one is iWave++, which adopts multi-level lifting schemes based on convolutional neural networks. However, iWave++ still has many unresolved problems. First, the independent entropy coding of each component makes it impossible to use the correlation between components better. Secondly, additive wavelet transform limits the nonlinear ability of learnable wavelet transform. Moreover, the offline training strategy makes the iWave++ unable to adjust according to the content. In this paper, we propose an improved framework for iWave++ called iWave-Pro. iWavePro is designed with several techniques to overcome the problems mentioned above. These techniques are the joint multi-component Gaussian mixture entropy coding, the affine wavelet-like transform, and the online training. Experimental results show that our method can save 10.73% bit rate compared with iWave++ at the same quality.
For current learned image compression methods, padding input images is necessary to meet the resolution requirements of down-sampling layers. However, the impact of padding has not been studied thoroughly. Most previo...
For current learned image compression methods, padding input images is necessary to meet the resolution requirements of down-sampling layers. However, the impact of padding has not been studied thoroughly. Most previous studies ignore padded images in the training process. In this paper, we analyze the impact of padding on compression performance. Then, we propose a padding-aware training (PAT) strategy, handling the padding effect during the training. Specifically, our PAT strategy calculates the loss of pre-padding image through a masking operation. Finally, according to our systematic experimental results, we find that images with different resolutions tend to favor different padding modes. Therefore, we further propose to conduct padding mode decision in the encoding process for rate-distortion optimization. Experiments demonstrate that our proposed PAT strategy and padding mode decision effectively compensate for the performance drop caused by padding.
The objective of this paper is to propose an empirical method to inverse significant wave height(SWH)under typhoon conditions from collected dual-polarization Gaofen(GF)-3 synthetic aperture radar(SAR)*** typhoon scen...
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The objective of this paper is to propose an empirical method to inverse significant wave height(SWH)under typhoon conditions from collected dual-polarization Gaofen(GF)-3 synthetic aperture radar(SAR)*** typhoon scenes were cap-tured from narrow scan(NSC)and wide scan(WSC)images,and collocated with European Center for Medium-Range Weather Fore-casts reanalysis data of(ECMWF).To improve the quality of GF-3 SAR images,the recalibration over rainforest and de-scalloping were carried *** establish the empirical relationship between SAR-derived parameters and collocated SWH,the sensitivity analysis of typical parameters about the normalized radar cross section(Nrcs)and imagery variance(Cvar)were performed to both VV and VH polarized *** scenes from GF-3 SAR imagery under typhoon conditions were used for training the model by the multivari-ate least square regression,and one scene was used for preliminary *** was found that the joint retrieval model based on VV and VH polarized SAR imagery performed better than any single polarized *** results,verified by using ECMWF data,revealed the soundness of this approach,with a correlation of 0.95,bias of 0 m,RMSE of 0.44 and SI of 0.01 when VV polarization and VH polarization data were both used.
Blind Image Quality Assessment (BIQA) is susceptible to poor transferability when the distribution shift occurs, e.g., from synthesis degradation to authentic degradation. To mitigate this, some studies have attempted...
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Almost all digital videos are coded into compact representations before being transmitted. Such compact representations need to be decoded back to pixels before being displayed to humans and – as usual – before bein...
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High Efficiency Video Coding (HEVC) achieves significant improvement in compression efficiency by introducing quadtree-based block partition. However, in the HEVC reference software-HM, the optimal partition is found ...
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The Segment Anything Model (SAM) exhibits remarkable versatility and zero-shot learning abilities, owing largely to its extensive training data (SA-1B). Recognizing SAM’s dependency on manual guidance given its categ...
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With the burgeoning advancements in the field of natural language processing (NLP), the demand for training data has increased significantly. To save costs, it has become common for users and businesses to outsource t...
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Composite regularization models are widely used in sparse signal processing, making multiple regularization parameters selection a significant problem to be solved. Variety kinds of composite regularization models are...
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