diff --git a/src/__tests__/__snapshots__/index.test.ts.snap b/src/__tests__/__snapshots__/index.test.ts.snap
index b7eb3cb7d..73653ede1 100644
--- a/src/__tests__/__snapshots__/index.test.ts.snap
+++ b/src/__tests__/__snapshots__/index.test.ts.snap
@@ -136,6 +136,7 @@ exports[`existence of exported functions 1`] = `
   "xy2ToXY",
   "xreimSortX",
   "xreimZeroFilling",
+  "xreimAutomaticBaselineRecognition",
   "xyArrayAlign",
   "xyArrayAlignToFirst",
   "xyArrayMerge",
diff --git a/src/utils/__tests__/createRandomArray.test.ts b/src/utils/__tests__/createRandomArray.test.ts
index 907fdd37c..0505160e2 100644
--- a/src/utils/__tests__/createRandomArray.test.ts
+++ b/src/utils/__tests__/createRandomArray.test.ts
@@ -63,7 +63,6 @@ test('Testing in conjunction with spectra-fitting', () => {
   });
 
   expect(fittedPeaks.peaks[0].x).toBeDeepCloseTo(10, 2);
-  //@ts-expect-error it is a gaussian shape
   expect(fittedPeaks.peaks[0].shape.fwhm).toBeDeepCloseTo(
     2 * Math.sqrt(2 * Math.log(2)),
     1,
diff --git a/src/xreim/__tests__/xreimAutomaticBaselineRecognition.test.ts b/src/xreim/__tests__/xreimAutomaticBaselineRecognition.test.ts
new file mode 100644
index 000000000..c53799c2a
--- /dev/null
+++ b/src/xreim/__tests__/xreimAutomaticBaselineRecognition.test.ts
@@ -0,0 +1,29 @@
+import { expect, test } from 'vitest';
+
+import { xreimAutomaticBaselineRecognition } from '../xreimAutomaticBaselineRecognition.ts';
+
+test('detects baseline regions around a Gaussian peak', () => {
+  const length = 201;
+  const x = new Float64Array(length);
+  const re = new Float64Array(length);
+  const im = new Float64Array(length);
+
+  for (let i = 0; i < length; i++) {
+    x[i] = i;
+    re[i] = 0.05 * i + 25 * Math.exp(-((i - 100) ** 2) / (2 * 15 ** 2));
+  }
+
+  const mask = xreimAutomaticBaselineRecognition(
+    { x, re, im },
+    { scale: 4, thresholdFactor: 0.5, erosionRadius: 0, component: 're' },
+  );
+
+  const maskArray = Array.from(mask);
+
+  expect(maskArray[0]).toBe(1);
+  expect(maskArray[200]).toBe(1);
+  expect(maskArray[100]).toBe(0);
+
+  expect(maskArray).toContain(1);
+  expect(maskArray).toContain(0);
+});
diff --git a/src/xreim/index.ts b/src/xreim/index.ts
index f987f0711..a7c71ea2a 100644
--- a/src/xreim/index.ts
+++ b/src/xreim/index.ts
@@ -1,2 +1,3 @@
 export * from './xreimSortX.ts';
 export * from './xreimZeroFilling.ts';
+export * from './xreimAutomaticBaselineRecognition.ts';
diff --git a/src/xreim/xreimAutomaticBaselineRecognition.ts b/src/xreim/xreimAutomaticBaselineRecognition.ts
new file mode 100644
index 000000000..a416b133e
--- /dev/null
+++ b/src/xreim/xreimAutomaticBaselineRecognition.ts
@@ -0,0 +1,242 @@
+import type { DoubleArray } from 'cheminfo-types';
+
+import type { DataXReIm } from '../types/index.ts';
+
+export interface AutomaticBaselineRecognitionOptions {
+  /**
+   * Scale parameter for the discrete CWT-Haar derivative approximation.
+   * Larger values reduce noise but widen the detected peaks.
+   * A value of 'auto' computes a heuristic scale based on the spectrum length.
+   * @default 'auto'
+   */
+  scale?: number | 'auto';
+
+  /**
+   * Multiplicative factor used in the iterative thresholding rule.
+   * The threshold is the mean plus factor × standard deviation.
+   * A value close to 0.5 is often more robust for the unnormalized CWT derivative
+   * used here than the 3 used in the original Dietrich procedure.
+   * @default 0.5
+   */
+  thresholdFactor?: number;
+
+  /**
+   * Radius of the 1D erosion filter used to remove isolated spikes in the mask.
+   * @default 1
+   */
+  erosionRadius?: number;
+
+  /**
+   * Signal component used for the analysis.
+   * - 're': use the real component.
+   * - 'im': use the imaginary component.
+   * - 'magnitude': use the magnitude of the complex signal.
+   * @default 're'
+   */
+  component?: 're' | 'im' | 'magnitude';
+}
+
+/**
+ * Automatically detects signal-free regions in a 1D spectrum using an
+ * approximate CWT-Haar derivative followed by iterative thresholding.
+ *
+ * The output is a binary mask where 1 marks points belonging to the baseline
+ * (signal-free regions) and 0 marks points associated with peaks or signal.
+ * @param data - object of kind {x:[], re:[], im:[]}
+ * @param options - recognition options
+ * @returns a binary mask as a Uint8Array
+ */
+export function xreimAutomaticBaselineRecognition(
+  data: Omit<DataXReIm, 'im'> & { im?: DoubleArray },
+  options: AutomaticBaselineRecognitionOptions = {},
+): Uint8Array {
+  const {
+    scale = 'auto',
+    thresholdFactor = 0.5,
+    erosionRadius = 1,
+    component = 're',
+  } = options;
+
+  const length = data.x.length;
+  if (data.re.length !== length || (data.im && data.im.length !== length)) {
+    throw new TypeError('length of x, re and im must be identical');
+  }
+
+  if (!data.im && component !== 're') {
+    throw new TypeError(
+      `component '${component}' requires im array to be defined`,
+    );
+  }
+
+  const signal = getSignal({ im: data.re, ...data }, component);
+  const actualScale = resolveScale(length, scale);
+
+  // OPTIMIZATION 1: O(N) derivative computation
+  const derivative = computeCwtHaarDerivative(signal, actualScale);
+
+  const power = new Float64Array(length);
+  for (let i = 0; i < length; i++) {
+    const value = derivative[i];
+    power[i] = value * value;
+  }
+
+  // OPTIMIZATION 2: Iterative threshold without array allocations
+  const threshold = iterativeThreshold(power, thresholdFactor);
+
+  const mask = new Uint8Array(length);
+  for (let i = 0; i < length; i++) {
+    mask[i] = power[i] <= threshold ? 1 : 0;
+  }
+
+  if (erosionRadius > 0) {
+    return erodeMask(mask, erosionRadius);
+  }
+  return mask;
+}
+
+function computeCwtHaarDerivative(
+  signal: DoubleArray,
+  scale: number,
+): Float64Array {
+  const length = signal.length;
+  const derivative = new Float64Array(length);
+  if (scale <= 0) return derivative;
+
+  // We want: sum_{j=1 to scale} (signal[i+j] - signal[i-j])
+  // This is: (sum of right window) - (sum of left window)
+  let leftSum = 0;
+  let rightSum = 0;
+
+  // Initialize windows for the first valid i (i = scale)
+  for (let j = 1; j <= scale; j++) {
+    leftSum += signal[scale - j];
+    rightSum += signal[scale + j];
+  }
+  derivative[scale] = (rightSum - leftSum) / scale;
+
+  for (let i = scale + 1; i < length - scale; i++) {
+    // Slide windows: subtract the element leaving and add the element entering
+    leftSum = leftSum - signal[i - scale - 1] + signal[i - 1];
+    rightSum = rightSum - signal[i + 1] + signal[i + scale + 1];
+    derivative[i] = (rightSum - leftSum) / scale;
+  }
+
+  return derivative;
+}
+
+function iterativeThreshold(values: Float64Array, factor: number): number {
+  let threshold = calculateThresholdFiltered(
+    values,
+    factor,
+    Number.POSITIVE_INFINITY,
+  );
+  let previousThreshold = Number.POSITIVE_INFINITY;
+
+  // Max iterations safety cap to prevent infinite loops in edge cases
+  let iterations = 0;
+  while (Math.abs(previousThreshold - threshold) > 1e-12 && iterations < 100) {
+    previousThreshold = threshold;
+    threshold = calculateThresholdFiltered(values, factor, threshold);
+    iterations++;
+  }
+
+  return threshold;
+}
+
+/**
+ * Calculates mean + factor * std, but only considers values <= currentThreshold
+ * This removes the need to create a new filtered array every iteration.
+ * @param values
+ * @param factor
+ * @param currentThreshold
+ */
+function calculateThresholdFiltered(
+  values: Float64Array,
+  factor: number,
+  currentThreshold: number,
+): number {
+  let sum = 0;
+  let count = 0;
+
+  for (const val of values) {
+    if (val <= currentThreshold) {
+      sum += val;
+      count++;
+    }
+  }
+
+  if (count === 0) return currentThreshold;
+
+  const mean = sum / count;
+  let varianceSum = 0;
+
+  for (const val of values) {
+    if (val <= currentThreshold) {
+      const diff = val - mean;
+      varianceSum += diff * diff;
+    }
+  }
+
+  const std = Math.sqrt(varianceSum / count);
+  return mean + factor * std;
+}
+
+function erodeMask(mask: Uint8Array, radius: number): Uint8Array {
+  const length = mask.length;
+  const result = new Uint8Array(length);
+  const windowSize = 2 * radius + 1;
+  const threshold = windowSize / 2;
+
+  for (let i = 0; i < length; i++) {
+    if (mask[i] === 0) continue;
+
+    let trueCount = 0;
+    for (let offset = -radius; offset <= radius; offset++) {
+      const index = i + offset;
+      if (index >= 0 && index < length && mask[index] === 1) {
+        trueCount++;
+      }
+    }
+    // Result is 1 only if majority of window is 1
+    result[i] = trueCount > threshold ? 1 : 0;
+  }
+
+  return result;
+}
+
+function getSignal(
+  data: DataXReIm,
+  component: 're' | 'im' | 'magnitude',
+): DoubleArray {
+  // Validate component when im is not provided
+
+  const { re, im } = data;
+  switch (component) {
+    case 'im':
+      return im;
+    case 'magnitude': {
+      const magnitude = new Float64Array(re.length);
+      for (let i = 0; i < re.length; i++) {
+        magnitude[i] = Math.hypot(re[i], im[i]);
+      }
+      return magnitude;
+    }
+    case 're':
+      return re;
+    default:
+      return re;
+  }
+}
+
+function resolveScale(
+  length: number,
+  scale: AutomaticBaselineRecognitionOptions['scale'],
+): number {
+  if (typeof scale === 'number') {
+    return Math.max(
+      1,
+      Math.min(Math.floor(scale), Math.floor((length - 1) / 2)),
+    );
+  }
+  return Math.max(1, Math.floor(length / 512));
+}