This paper extends Daubechies' frame criterion for one-dimensional wavelets to two dimensions and computes the frame bounds for 2D Gabor wavelets. The completeness criteria for 2D Gabor image representations are important due to their increasing role in computer vision applications and modeling biological vision. Recent neurophysiological evidence suggests that the filter response profiles of simple cells in the visual cortex are best modeled as a family of self-similar 2D Gabor wavelets. The paper derives conditions under which a set of continuous 2D Gabor wavelets can provide a complete representation of any image and finds self-similar wavelet parameterizations that allow stable reconstruction by summation, similar to an orthonormal basis. The approximation of a "tight frame" generates redundancy, allowing low-resolution neural responses to represent high-resolution images. The paper demonstrates these theoretical insights through image reconstruction experiments.This paper extends Daubechies' frame criterion for one-dimensional wavelets to two dimensions and computes the frame bounds for 2D Gabor wavelets. The completeness criteria for 2D Gabor image representations are important due to their increasing role in computer vision applications and modeling biological vision. Recent neurophysiological evidence suggests that the filter response profiles of simple cells in the visual cortex are best modeled as a family of self-similar 2D Gabor wavelets. The paper derives conditions under which a set of continuous 2D Gabor wavelets can provide a complete representation of any image and finds self-similar wavelet parameterizations that allow stable reconstruction by summation, similar to an orthonormal basis. The approximation of a "tight frame" generates redundancy, allowing low-resolution neural responses to represent high-resolution images. The paper demonstrates these theoretical insights through image reconstruction experiments.