/**
 * Convolution Operations
 *
 * Implements 1D and 2D convolution using both direct and FFT methods.
 *
 * @packageDocumentation
 */
/**
 * Convolution mode - determines output size
 */
export type ConvMode = 'full' | 'same' | 'valid';
/**
 * Direct 1D convolution (for small inputs)
 *
 * @param x - Input signal
 * @param h - Convolution kernel
 * @param mode - Output mode: 'full', 'same', or 'valid'
 * @returns Convolution result
 */
export declare function convDirect(x: number[] | Float64Array, h: number[] | Float64Array, mode?: ConvMode): number[];
/**
 * FFT-based 1D convolution (efficient for large inputs)
 *
 * Uses the convolution theorem: conv(x,h) = IFFT(FFT(x) * FFT(h))
 *
 * @param x - Input signal
 * @param h - Convolution kernel
 * @param mode - Output mode: 'full', 'same', or 'valid'
 * @returns Convolution result
 */
export declare function convFFT(x: number[] | Float64Array, h: number[] | Float64Array, mode?: ConvMode): number[];
/**
 * 1D Convolution with automatic method selection
 *
 * Automatically selects direct or FFT method based on input sizes.
 *
 * @param x - Input signal
 * @param h - Convolution kernel
 * @param mode - Output mode: 'full' (default), 'same', or 'valid'
 * @returns Convolution result
 *
 * @example
 * ```typescript
 * // Simple convolution
 * const signal = [1, 2, 3, 4, 5];
 * const kernel = [1, 0, -1]; // Edge detection
 * const result = conv(signal, kernel, 'same');
 * ```
 */
export declare function conv(x: number[] | Float64Array, h: number[] | Float64Array, mode?: ConvMode): number[];
/**
 * Cross-correlation of two signals
 *
 * Cross-correlation is convolution with the kernel reversed.
 *
 * @param x - First signal
 * @param h - Second signal
 * @param mode - Output mode
 * @returns Cross-correlation result
 */
export declare function xcorr(x: number[] | Float64Array, h: number[] | Float64Array, mode?: ConvMode): number[];
/**
 * Auto-correlation of a signal
 *
 * @param x - Input signal
 * @param mode - Output mode
 * @returns Auto-correlation result
 */
export declare function autocorr(x: number[] | Float64Array, mode?: ConvMode): number[];
/**
 * Direct 2D convolution
 *
 * @param image - 2D input array
 * @param kernel - 2D convolution kernel
 * @param mode - Output mode
 * @returns 2D convolution result
 */
export declare function conv2Direct(image: number[][], kernel: number[][], mode?: ConvMode): number[][];
/**
 * FFT-based 2D convolution
 *
 * @param image - 2D input array
 * @param kernel - 2D convolution kernel
 * @param mode - Output mode
 * @returns 2D convolution result
 */
export declare function conv2FFT(image: number[][], kernel: number[][], mode?: ConvMode): number[][];
/**
 * 2D Convolution with automatic method selection
 *
 * @param image - 2D input array
 * @param kernel - 2D convolution kernel
 * @param mode - Output mode: 'full' (default), 'same', or 'valid'
 * @returns 2D convolution result
 *
 * @example
 * ```typescript
 * // Edge detection with Sobel kernel
 * const image = [
 *   [1, 2, 3],
 *   [4, 5, 6],
 *   [7, 8, 9],
 * ];
 * const sobelX = [
 *   [-1, 0, 1],
 *   [-2, 0, 2],
 *   [-1, 0, 1],
 * ];
 * const edges = conv2(image, sobelX, 'same');
 * ```
 */
export declare function conv2(image: number[][], kernel: number[][], mode?: ConvMode): number[][];
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