How do you solve Image Smoother on LeetCode?

Image Smoother (LeetCode #661) can be solved using Brute Force or Optimal Solution. The optimal approach is Optimal Solution with O(m * n) time complexity and O(m * n) space complexity. Key insight: Understanding how to handle edge cases (corners and edges) is crucial.

What is the brute force solution for Image Smoother?

The brute force approach for Image Smoother is Brute Force, with O(m * n) time complexity and O(m * n) space complexity. The brute-force approach involves iterating through each pixel of the image and calculating the average of the pixel and its surrounding neighbors. This is straightforward but can be slow due to repeated calculations. The optimal Optimal Solution approach improves this to O(m * n).

What algorithm or data structure is used to solve Image Smoother?

Image Smoother uses the following concepts: Array, Matrix. The recommended patterns to study are: Matrix Traversal, Sliding Window.

What are the interview tips for Image Smoother?

Always clarify the problem statement and constraints before diving into coding. Think aloud during the interview to show your thought process. Consider edge cases and test your solution with them.

What are common mistakes when solving Image Smoother?

Failing to account for boundary conditions when accessing neighboring cells. Not using integer division correctly when calculating the average.

#661

Image Smoother

Easy

ArrayMatrixMatrix TraversalSliding Window

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

	Brute Force	Optimal Solution★
Time	O(m * n)	O(m * n)
Space	O(m * n)	O(m * n)

💡

Intuition

Time O(m * n)Space O(m * n)

The optimal approach is similar to the brute force but focuses on minimizing redundant calculations by using a single pass to compute the sum and count of valid neighbors. This reduces the number of iterations needed.

⚙️

Algorithm

3 steps

1Step 1: Create a new matrix of the same size to store the smoothed values.
2Step 2: For each pixel, calculate the total and count of valid neighbors in a single nested loop.
3Step 3: Store the computed average in the new matrix after processing all pixels.

solution.py16 lines

1# Full working Python code
2
3def imageSmoother(img):
4    m, n = len(img), len(img[0])
5    result = [[0] * n for _ in range(m)]
6    for i in range(m):
7        for j in range(n):
8            total, count = 0, 0
9            for di in range(-1, 2):
10                for dj in range(-1, 2):
11                    ni, nj = i + di, j + dj
12                    if 0 <= ni < m and 0 <= nj < n:
13                        total += img[ni][nj]
14                        count += 1
15            result[i][j] = total // count
16    return result

ℹ

Complexity note: The time complexity remains O(m * n) as we still process each pixel, but we ensure each pixel's neighbors are counted efficiently. Space complexity is O(m * n) for the result matrix.

1Understanding how to handle edge cases (corners and edges) is crucial.
2Recognizing that the average should only consider valid neighboring cells can help simplify the logic.

Solutions and explanations are original Tejav content. Problem titles © LeetCode — use the LeetCode button above for the full problem statement.