Stereo Video Deblurring Eval

stereo-video-deblurring-eval

Stereo Video Deblurring — Sellent et al. (2016) (arXiv:1607.08421, 2016)

What this evaluates

Evaluates the ability of a stereo video deblurring algorithm to recover sharp frames from motion-blurred inputs, specifically testing its robustness to spatially-variant blur caused by independent 3D object motion and non-planar surfaces.

Datasets

• Custom synthetic raytraced & real stereo captures — total ?; splits: test (-1)

Metrics

• PSNR (primary) — range: other
  • Peak Signal-to-Noise Ratio computed between the deblurred output and the ground-truth sharp reference frame. Standard logarithmic ratio of max pixel intensity squared to mean squared error.
• AEP — range: other
  • Average Endpoint Error measuring the mean Euclidean distance between estimated and ground-truth 2D motion vectors.
• ADE — range: other
  • Average Disparity Error measuring the mean absolute difference between estimated and ground-truth disparity maps.

Input / output format

Input: Blurred left and right stereo video frames (640x480), camera calibration parameters, and exposure times. For synthetic data, ground-truth sharp frames and known 3D motion/disparity are provided.

Output: Deblurred left and right stereo frames, estimated scene flow/disparity maps, and motion boundary masks.

Scoring recipe

def compute_psnr(gt, pred):
    mse = np.mean((gt - pred) ** 2)
    if mse == 0:
        return 100.0
    max_pixel = 255.0
    return 10 * np.log10((max_pixel ** 2) / mse)

def compute_aep(gt_flow, pred_flow):
    return np.mean(np.sqrt(np.sum((gt_flow - pred_flow) ** 2, axis=-1)))

def compute_ade(gt_disp, pred_disp):
    return np.mean(np.abs(gt_disp - pred_disp))

Common pitfalls

• Using 2D optical flow instead of 3D scene flow/homographies for non-fronto-parallel motion leads to significant accuracy drops and ringing artifacts.
• Disparity estimation failures on complex, non-planar objects (e.g., 'apples') can degrade homography-based deblurring unless motion discontinuities are explicitly masked and downweighted.
• Assuming constant linear velocity for 3D accelerated motion (like forward translation) causes blur kernel misalignment, which homography-based models handle better.

Evidence (verbatim from paper)

Table 2 shows the peak-signal-to-noise-ratio (PSNR) of the deblurred images from the different methods. We observe that the PSNR of our homography-based stereo video deblurring outperforms the results of deblurring with ground truth 2D displacement in all cases of non-fronto-parallel motion.

Citation

@misc{sellent2016stereo,
  title={Stereo Video Deblurring},
  author={Sellent et al. (2016)},
  year={2016},
  note={arXiv:1607.08421}
}

• arXiv: 1607.08421