fix: CLAHE tile size and alpha channel corruption in image enhancement

CLAHE width/height is tile size in pixels, not tile count. A 3px tile on
a 992x1088 image created ~330x360 independent histogram regions, producing
crosshatch/etching artifacts. Now uses image_dimension/8 (clamped 8-256)
for ~8 tiles per axis. Also strips alpha before enhancement and re-joins
after to prevent CLAHE/normalise/linear from corrupting transparency.

apps/api/src/routes/tools/image-enhancement.ts

@@ -41,8 +41,18 @@ async function processImageEnhancement(
   const outputFormat = await resolveOutputFormat(inputBuffer, filename);
   const analysis = await analyzeImage(inputBuffer);
   const meta = await sharp(inputBuffer).metadata();
+  const hasAlpha = meta.hasAlpha === true;
+
+  let alphaBuffer: Buffer | undefined;
+  if (hasAlpha) {
+    alphaBuffer = await sharp(inputBuffer).extractChannel(3).toBuffer();
+  }
 
   let image = sharp(inputBuffer);
+  if (hasAlpha) {
+    image = image.removeAlpha();
+  }
+
   image = applyCorrections(
     image,
     analysis.corrections,
@@ -56,6 +66,13 @@ async function processImageEnhancement(
     .toFormat(outputFormat.format, { quality: outputFormat.quality })
     .toBuffer();
 
+  if (alphaBuffer) {
+    buffer = await sharp(buffer)
+      .joinChannel(alphaBuffer)
+      .toFormat(outputFormat.format, { quality: outputFormat.quality })
+      .toBuffer();
+  }
+
   if (settings.deepEnhance && isToolInstalled("noise-removal")) {
     try {
       const jobId = randomUUID();

packages/image-engine/src/operations/auto-enhance.ts

@@ -226,10 +226,12 @@ export function applyCorrections(
   // maxSlope must be an integer (Sharp requirement); skip for tiny images
   if (toggles.contrast !== false) {
     const maxSlope = clamp(Math.round(1.0 + (intensity / 100) * 4.0 * presets.clahe), 1, 10);
-    const minDim = imageSize ? Math.min(imageSize.width, imageSize.height) : 4;
-    const tileSize = minDim >= 3 ? 3 : 1;
-    if (maxSlope >= 2) {
-      result = result.clahe({ width: tileSize, height: tileSize, maxSlope });
+    const w = imageSize?.width ?? 64;
+    const h = imageSize?.height ?? 64;
+    const tileW = clamp(Math.round(w / 8), 8, 256);
+    const tileH = clamp(Math.round(h / 8), 8, 256);
+    if (maxSlope >= 2 && w >= tileW && h >= tileH) {
+      result = result.clahe({ width: tileW, height: tileH, maxSlope });
     }
   }
 

tests/integration/image-enhancement.test.ts

@@ -530,6 +530,83 @@ describe("HEIC input enhancement", () => {
   });
 });
 
+// ── Alpha channel preservation ─────────────────────────────────
+describe("Alpha channel preservation", () => {
+  it("preserves alpha channel without crosshatch corruption", async () => {
+    const rgbaBuffer = await sharp({
+      create: {
+        width: 100,
+        height: 100,
+        channels: 4,
+        background: { r: 80, g: 120, b: 60, alpha: 1 },
+      },
+    })
+      .png()
+      .toBuffer();
+
+    const res = await postTool(
+      { mode: "auto", intensity: 80 },
+      rgbaBuffer,
+      "rgba.png",
+      "image/png",
+    );
+    expect(res.statusCode).toBe(200);
+    const result = JSON.parse(res.body);
+
+    const dlRes = await app.inject({
+      method: "GET",
+      url: result.downloadUrl,
+      headers: { authorization: `Bearer ${adminToken}` },
+    });
+    const { data, info } = await sharp(dlRes.rawPayload)
+      .ensureAlpha()
+      .raw()
+      .toBuffer({ resolveWithObject: true });
+    for (let i = 3; i < data.length; i += 4) {
+      expect(data[i]).toBe(255);
+    }
+  });
+
+  it("preserves partial transparency in PNG", async () => {
+    const semiTransparent = await sharp({
+      create: {
+        width: 50,
+        height: 50,
+        channels: 4,
+        background: { r: 100, g: 100, b: 100, alpha: 0.5 },
+      },
+    })
+      .png()
+      .toBuffer();
+
+    const res = await postTool(
+      { mode: "auto", intensity: 50 },
+      semiTransparent,
+      "semi.png",
+      "image/png",
+    );
+    expect(res.statusCode).toBe(200);
+    const result = JSON.parse(res.body);
+
+    const dlRes = await app.inject({
+      method: "GET",
+      url: result.downloadUrl,
+      headers: { authorization: `Bearer ${adminToken}` },
+    });
+    const meta = await sharp(dlRes.rawPayload).metadata();
+    expect(meta.channels).toBe(4);
+
+    const { data, info } = await sharp(dlRes.rawPayload)
+      .raw()
+      .toBuffer({ resolveWithObject: true });
+    const alphaValues = new Set<number>();
+    for (let i = 3; i < data.length; i += info.channels) {
+      alphaValues.add(data[i]);
+    }
+    expect(alphaValues.size).toBe(1);
+  });
+});
+
 // ── Large file handling ─────────────────────────────────────────
 describe("Large file handling", () => {
   it("enhances a large stress image", async () => {