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About this paper
Quanjun Li, Weixuan Li, Han Xia, Junhua Zhou, Chi-Man Pun, Xuhang Chen
RGA-Net reconstructs clearer images from smoke-obscured robotic-surgery endoscopy by combining local spatial and frequency-based processing in an encoder–decoder with reciprocal feature modulation between its paths. On DesmokeData, it achieves 30.258 dB PSNR and 0.945 SSIM, versus DehazeFormer’s 29.416 dB and 0.938. These paired-image results measure restoration quality, not clinical or real-time surgical benefits.
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