Using convolution for horizontal edge detection.

convolve.ts

// 5x5 image
const image = [
    [1, 2, 3, 4, 5],
    [5, 4, 3, 2, 1],
    [0, 2, 4, 6, 8],
    [8, 6, 4, 2, 0],
    [3, 4, 5, 2, 1],
];

// seeded for horizontal edge-detection
const kernel_x = [
    [-1, 0, 1],
    [-2, 0, 2],
    [-1, 0, 1],
];

type Point = { row: number, col: number };

function convolve_step(image: number[][], center: Point, kernel: number[][]) {
    return (
        // top
        (image[center.row - 1]?.[center.col - 1] ?? 0) * kernel[0][0] + 
        (image[center.row - 1]?.[center.col] ?? 0) * kernel[0][1] +
        (image[center.row - 1]?.[center.col + 1] ?? 0) * kernel[0][2] +
        // middle
        (image[center.row]?.[center.col - 1] ?? 0) * kernel[1][0] + 
        (image[center.row]?.[center.col] ?? 0) * kernel[1][1] +
        (image[center.row]?.[center.col + 1] ?? 0) * kernel[1][2] +
        // bottom
        (image[center.row + 1]?.[center.col - 1] ?? 0) * kernel[1][0] + 
        (image[center.row + 1]?.[center.col] ?? 0) * kernel[1][1] +
        (image[center.row + 1]?.[center.col + 1] ?? 0) * kernel[1][2]
    );
}

const output = [
    [0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0],
]
for (let row = 0; row < 5; row += 1) {
    for (let col = 0; col < 5; col += 1) {
        output[row][col] = convolve_step(image, {row, col}, kernel_x);
    }
}