iv_backproject_superres — 2D `restoration` op

• Data kinds: image → image

• Call: fullseye.apply(img, "iv_backproject_superres", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])

Usage

Single-image iterative back-projection super-resolution (Irani-Peleg). Upscale to a higher grid, simulate the low-res observation by blurring and downscaling, back-project the residual into the high grid, then downscale the consistent estimate back to HxW. Net effect: high-frequency detail is boosted (sharpening-by-consistency). `a sets iterations n = 1 + round(a*4) (1..5); b` sets the back-projection step gain g = 0.5 + b (0.5..1.5).

Detailed usage guide

• gallery2d_smoothing_rank family guide

References (sample data, literature)

• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).

• Operator provenance and references — the sources of the research/methods this op family came from.

• The canonical algorithm (author, year) and its uses are named in the family usage guide above.

Runnable examples (verified samples that actually call this op)

• gallery2d_smoothing_rank — py -3.11 examples/gallery2d_smoothing_rank.py

Ops the type connects to (they accept `image` as input)

identity · gaussian · mean_box · bilateral · unsharp · median · min_filter · max_filter

Same category (`restoration`)

xsk_inpaint · xsk_richardson_lucy · xsk_unwrap_phase · xcv_inpaint · xsk2_wiener · xcv3_inpaint_ns · iv_richardson_lucy · iv_wiener_deconv_spatial

*Provenance: ops.py — 2D operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*

iv_backproject_superres — 2D restoration op