dc_retinex — 2D `decomposition` op

• Data kinds: image → image

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

dc_retinex: 入力 → 出力

*The figure is the real output on a synthetic 128×128 input. Left: input, right: output (a non-image return value is shown as the value itself).*

Usage

Single-scale retinex reflectance: log(I) - log(Gauss_sigma(I)) -> [0,1].

Fixed log-domain gain (b) about mid-gray, NOT per-image min-max (which would

re-inflate an already-flat image); a sets the Gaussian scale.

Detailed usage guide

• gallery2d_texture_freq family guide

Background guides (the physics and conventions behind this op)

• blas_threads_and_memory — 行列分解が遅い理由の知識 — BLAS スレッド・キャッシュ・メモリ配置

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.

Try it in Studio

The program below has been verified to run (same input as the figure). In Studio's help this block becomes buttons that load and run it on the spot.

dc_retinex 0.50 0.50

▸ Load this pipeline · Load & run

Runnable examples (verified samples that actually call this op)

• gallery2d_texture_freq — py -3.11 examples/gallery2d_texture_freq.py

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

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

Same category (`decomposition`)

dc_structure_texture · dc_texture_residual · dc_rpca_lowrank · dc_rpca_sparse · dc_local_contrast_norm · dc_homomorphic

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

dc_retinex — 2D decomposition op