LoopyLuci Computational Photography
Implementing computational photography pipelines — from HDR merging and burst processing through denoising, super-resolution, and neural rendering.
When to Use
- Building camera apps with advanced features
- Implementing HDR, night mode, or portrait mode
- Image enhancement (denoising, super-resolution)
- Computational imaging pipelines
- Neural rendering and image synthesis
Photography Pipeline
import cv2
import numpy as np
class ComputationalPhotography:
"""Computational photography pipeline components."""
@staticmethod
def hdr_merge(images: List[np.array], exposures: List[float]) -> np.array:
"""Merge multiple exposures into HDR image."""
merge_debevec = cv2.createMergeDebevec()
hdr = merge_debevec.process(images, times=np.array(exposures))
tonemap = cv2.createTonemapReinhard(gamma=2.2)
ldr = tonemap.process(hdr)
return np.clip(ldr * 255, 0, 255).astype(np.uint8)
@staticmethod
def burst_denoise(burst: List[np.array]) -> np.array:
"""Denoise by averaging aligned burst frames."""
aligned = []
for i, frame in enumerate(burst):
if i == 0:
aligned.append(frame)
else:
# Align frames (simplified)
warp_matrix = np.eye(2, 3, dtype=np.float32)
aligned_frame = cv2.warpAffine(frame, warp_matrix,
(frame.shape[1], frame.shape[0]))
aligned.append(aligned_frame)
return np.mean(aligned, axis=0).astype(np.uint8)
Verification Checklist
- Image capture pipeline defined (raw → processed → output)
- HDR merging working across exposure brackets
- Denoising effective (PSNR, SSIM vs ground truth)
- Super-resolution produces real detail (not just sharpening)
- Alignment robust to handheld motion
- Processing time < capture interval for real-time
- Memory-efficient processing for high-resolution images
- Neural methods (if used) have acceptable latency