LeetCode #661 — EASY

Image Smoother

Build confidence with an intuition-first walkthrough focused on array fundamentals.

The Problem

Problem Statement

An image smoother is a filter of the size 3 x 3 that can be applied to each cell of an image by rounding down the average of the cell and the eight surrounding cells (i.e., the average of the nine cells in the blue smoother). If one or more of the surrounding cells of a cell is not present, we do not consider it in the average (i.e., the average of the four cells in the red smoother).

Given an m x n integer matrix img representing the grayscale of an image, return the image after applying the smoother on each cell of it.

Example 1:

Input: img = [[1,1,1],[1,0,1],[1,1,1]]
Output: [[0,0,0],[0,0,0],[0,0,0]]
Explanation:
For the points (0,0), (0,2), (2,0), (2,2): floor(3/4) = floor(0.75) = 0
For the points (0,1), (1,0), (1,2), (2,1): floor(5/6) = floor(0.83333333) = 0
For the point (1,1): floor(8/9) = floor(0.88888889) = 0

Example 2:

Input: img = [[100,200,100],[200,50,200],[100,200,100]]
Output: [[137,141,137],[141,138,141],[137,141,137]]
Explanation:
For the points (0,0), (0,2), (2,0), (2,2): floor((100+200+200+50)/4) = floor(137.5) = 137
For the points (0,1), (1,0), (1,2), (2,1): floor((200+200+50+200+100+100)/6) = floor(141.666667) = 141
For the point (1,1): floor((50+200+200+200+200+100+100+100+100)/9) = floor(138.888889) = 138

Constraints:

m == img.length
n == img[i].length
1 <= m, n <= 200
0 <= img[i][j] <= 255

Step 01

Brute Force Baseline

Problem summary: An image smoother is a filter of the size 3 x 3 that can be applied to each cell of an image by rounding down the average of the cell and the eight surrounding cells (i.e., the average of the nine cells in the blue smoother). If one or more of the surrounding cells of a cell is not present, we do not consider it in the average (i.e., the average of the four cells in the red smoother). Given an m x n integer matrix img representing the grayscale of an image, return the image after applying the smoother on each cell of it.

Baseline thinking

Start with the most direct exhaustive search. That gives a correctness anchor before optimizing.

Pattern signal: Array

Example 1

[[1,1,1],[1,0,1],[1,1,1]]

Example 2

[[100,200,100],[200,50,200],[100,200,100]]

Step 02

Core Insight

What unlocks the optimal approach

No official hints in dataset. Start from constraints and look for a monotonic or reusable state.

Interview move: turn each hint into an invariant you can check after every iteration/recursion step.

Step 03

Algorithm Walkthrough

Iteration Checklist

Define state (indices, window, stack, map, DP cell, or recursion frame).
Apply one transition step and update the invariant.
Record answer candidate when condition is met.
Continue until all input is consumed.

Use the first example testcase as your mental trace to verify each transition.

Step 04

Edge Cases

Minimum Input

Single element / shortest valid input

Validate boundary behavior before entering the main loop or recursion.

Duplicates & Repeats

Repeated values / repeated states

Decide whether duplicates should be merged, skipped, or counted explicitly.

Extreme Constraints

Upper-end input sizes

Re-check complexity target against constraints to avoid time-limit issues.

Invalid / Corner Shape

Empty collections, zeros, or disconnected structures

Handle special-case structure before the core algorithm path.

Step 05

Full Annotated Code

Source-backed implementations are provided below for direct study and interview prep.

// Accepted solution for LeetCode #661: Image Smoother
class Solution {
    public int[][] imageSmoother(int[][] img) {
        int m = img.length;
        int n = img[0].length;
        int[][] ans = new int[m][n];
        for (int i = 0; i < m; ++i) {
            for (int j = 0; j < n; ++j) {
                int s = 0;
                int cnt = 0;
                for (int x = i - 1; x <= i + 1; ++x) {
                    for (int y = j - 1; y <= j + 1; ++y) {
                        if (x >= 0 && x < m && y >= 0 && y < n) {
                            ++cnt;
                            s += img[x][y];
                        }
                    }
                }
                ans[i][j] = s / cnt;
            }
        }
        return ans;
    }
}

// Accepted solution for LeetCode #661: Image Smoother
func imageSmoother(img [][]int) [][]int {
	m, n := len(img), len(img[0])
	ans := make([][]int, m)
	for i, row := range img {
		ans[i] = make([]int, n)
		for j := range row {
			s, cnt := 0, 0
			for x := i - 1; x <= i+1; x++ {
				for y := j - 1; y <= j+1; y++ {
					if x >= 0 && x < m && y >= 0 && y < n {
						cnt++
						s += img[x][y]
					}
				}
			}
			ans[i][j] = s / cnt
		}
	}
	return ans
}

# Accepted solution for LeetCode #661: Image Smoother
class Solution:
    def imageSmoother(self, img: List[List[int]]) -> List[List[int]]:
        m, n = len(img), len(img[0])
        ans = [[0] * n for _ in range(m)]
        for i in range(m):
            for j in range(n):
                s = cnt = 0
                for x in range(i - 1, i + 2):
                    for y in range(j - 1, j + 2):
                        if 0 <= x < m and 0 <= y < n:
                            cnt += 1
                            s += img[x][y]
                ans[i][j] = s // cnt
        return ans

// Accepted solution for LeetCode #661: Image Smoother
impl Solution {
    pub fn image_smoother(img: Vec<Vec<i32>>) -> Vec<Vec<i32>> {
        let m = img.len();
        let n = img[0].len();
        let mut ans = vec![vec![0; n]; m];
        for i in 0..m {
            for j in 0..n {
                let mut s = 0;
                let mut cnt = 0;
                for x in i.saturating_sub(1)..=(i + 1).min(m - 1) {
                    for y in j.saturating_sub(1)..=(j + 1).min(n - 1) {
                        s += img[x][y];
                        cnt += 1;
                    }
                }
                ans[i][j] = s / cnt;
            }
        }
        ans
    }
}

// Accepted solution for LeetCode #661: Image Smoother
function imageSmoother(img: number[][]): number[][] {
    const m = img.length;
    const n = img[0].length;
    const ans: number[][] = Array.from({ length: m }, () => Array(n).fill(0));
    for (let i = 0; i < m; ++i) {
        for (let j = 0; j < n; ++j) {
            let s = 0;
            let cnt = 0;
            for (let x = i - 1; x <= i + 1; ++x) {
                for (let y = j - 1; y <= j + 1; ++y) {
                    if (x >= 0 && x < m && y >= 0 && y < n) {
                        ++cnt;
                        s += img[x][y];
                    }
                }
            }
            ans[i][j] = Math.floor(s / cnt);
        }
    }
    return ans;
}

Step 06

Interactive Study Demo

Use this to step through a reusable interview workflow for this problem.

Custom checkpoints (one per line)

Press Step or Run All to begin.

Step 07

Complexity Analysis

Time

O(n)

Space

O(1)

Approach Breakdown

BRUTE FORCE

O(n²) time

O(1) space

Two nested loops check every pair or subarray. The outer loop fixes a starting point, the inner loop extends or searches. For n elements this gives up to n²/2 operations. No extra space, but the quadratic time is prohibitive for large inputs.

OPTIMIZED

O(n) time

O(1) space

Most array problems have an O(n²) brute force (nested loops) and an O(n) optimal (single pass with clever state tracking). The key is identifying what information to maintain as you scan: a running max, a prefix sum, a hash map of seen values, or two pointers.

Shortcut: If you are using nested loops on an array, there is almost always an O(n) solution. Look for the right auxiliary state.

Coach Notes

Common Mistakes

Review these before coding to avoid predictable interview regressions.

Off-by-one on range boundaries

Wrong move: Loop endpoints miss first/last candidate.

Usually fails on: Fails on minimal arrays and exact-boundary answers.

Fix: Re-derive loops from inclusive/exclusive ranges before coding.