USEnhance2023 Aligned Pixel aligned low quality / high quality ultrasound image pairs derived from the USEnhance2023 Grand Challenge. This dataset is a release artifact of the paper Style Driven Data Synthesis and Degradation Aware Enhancement for Ultrasound Image Restoration and is intended for academic research only . Why this dataset The raw USEnhance2023 corpus contains low quality/ and high quality/ ultrasound scans, but the two splits are acquired from different patients with different probes — they are unpaired . This blocks supervised pixel level training objectives (L1 / L2 / LPIPS) that need ground truth correspondence. We use a CycleDiff (cycle consistent latent diffusion) pipeline to translate each high quality image into a pixel aligned synthetic low quality counterpart, producing the Aligned LQ/ split. This makes USEnhance2023 directly usable for supervised image restoration / super resolution / enhancement research. Contents Split Count Resolution Bit depth Source train/GT/ 840 256×256 8 bit grayscale Raw USEnhance2023 high quality train/LQ/ 840 256×256 8 bit grayscale Raw USEnhance2023 low quality (real, unpaired) train/Aligned LQ/ 840 256×256 8 bit grayscale CycleD…
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