Normalize Image

Implement a function to normalize image pixel values to the range [0, 1]. Normalization is a crucial preprocessing step in Computer Vision that scales the intensity values of an image to a common range, which helps in reducing the impact of illumination changes and improving the robustness of subsequent processing tasks. The normalization process involves shifting and scaling the original pixel values $I$ using the minimum $I_{min}$ and maximum $I_{max}$ values in the image. Here are the steps to achieve this:

Find the minimum and maximum pixel values in the image.
Apply the normalization formula to each pixel value. The key formula for normalization is $I_{norm} = \frac{I - I_{min}}{I_{max} - I_{min}}$ This technique is widely used in image processing pipelines.

Example:

Input:

normalize([[0, 128, 255]])

Output:

[[0.0, 0.502, 1.0]]

Reasoning:

(128-0)/(255-0) ≈ 0.502

Constraints:

Input is a 2D grayscale image
If all pixels are identical, return array of 0.0 values
Return values rounded to 4 decimal places

Example:

Constraints:

Test Results