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Browse and apply convolution kernels with live previews
O(x,y) = Σᵢ Σⱼ I(x+i, y+j) · K(i,j)A convolution kernel is a small matrix (typically 3×3 or 5×5) that slides across an image, multiplying and summing pixel values at each position to produce transformations like blur, sharpen, and edge detection.
Popular kernels include Gaussian blur (smoothing via bell-curve weights), Sobel (horizontal/vertical edge detection), Laplacian (contour finding), and emboss (3D relief effect).
// Identity Sharpen Edge Detect
[0, 0, 0] [ 0,-1, 0] [-1,-1,-1]
[0, 1, 0] [-1, 5,-1] [-1, 8,-1]
[0, 0, 0] [ 0,-1, 0] [-1,-1,-1]