This paper extends the frame criterion for one-dimensional wavelets to two dimensions and computes the frame bounds for 2D Gabor wavelets. The study highlights the importance of completeness criteria for 2D Gabor image representations due to their role in computer vision and modeling biological vision. The paper derives conditions under which a set of continuous 2D Gabor wavelets provides a complete representation of any image and identifies self-similar wavelet parameterizations that allow stable reconstruction by summation. It also shows how approximating a "tight frame" generates redundancy, enabling low-resolution neural responses to represent high-resolution images, as demonstrated by image reconstruction experiments with severely quantized 2D Gabor coefficients. The paper introduces a family of 2D Gabor wavelets that satisfy wavelet theory and neurophysiological constraints for simple cells. These wavelets are derived from a general 2D complex Gabor function, normalized to have zero mean. The wavelets are constrained by physiological findings, including the aspect ratio of the elliptical Gaussian envelope, the direction of the plane wave, and the bandwidth of the frequency response. The paper also derives the frame bounds for these wavelets, showing that the phase space sampling density provided by simple cells in the primary visual cortex is sufficient to form an almost tight frame, allowing stable reconstruction of images using linear superposition of Gabor wavelets with their own projection coefficients. The paper generalizes Daubechies' frame criterion to 2D and derives the frame bounds for 2D Gabor wavelets. It shows that the frame bounds depend on the sampling density in phase space and the number of sampling orientations. The paper also discusses the advantages of suboctave sampling, which makes the frame tighter, and presents frame bounds for fractionally dilated 2D wavelets. The results show that the frame bounds for 2D Gabor wavelets depend on the sampling density, the number of sampling orientations, and the frequency steps per octave. The paper concludes that the frame bounds for 2D Gabor wavelets are determined by these parameters and that the wavelets can form a frame under certain conditions. The results are supported by image reconstruction experiments and theoretical analysis.This paper extends the frame criterion for one-dimensional wavelets to two dimensions and computes the frame bounds for 2D Gabor wavelets. The study highlights the importance of completeness criteria for 2D Gabor image representations due to their role in computer vision and modeling biological vision. The paper derives conditions under which a set of continuous 2D Gabor wavelets provides a complete representation of any image and identifies self-similar wavelet parameterizations that allow stable reconstruction by summation. It also shows how approximating a "tight frame" generates redundancy, enabling low-resolution neural responses to represent high-resolution images, as demonstrated by image reconstruction experiments with severely quantized 2D Gabor coefficients. The paper introduces a family of 2D Gabor wavelets that satisfy wavelet theory and neurophysiological constraints for simple cells. These wavelets are derived from a general 2D complex Gabor function, normalized to have zero mean. The wavelets are constrained by physiological findings, including the aspect ratio of the elliptical Gaussian envelope, the direction of the plane wave, and the bandwidth of the frequency response. The paper also derives the frame bounds for these wavelets, showing that the phase space sampling density provided by simple cells in the primary visual cortex is sufficient to form an almost tight frame, allowing stable reconstruction of images using linear superposition of Gabor wavelets with their own projection coefficients. The paper generalizes Daubechies' frame criterion to 2D and derives the frame bounds for 2D Gabor wavelets. It shows that the frame bounds depend on the sampling density in phase space and the number of sampling orientations. The paper also discusses the advantages of suboctave sampling, which makes the frame tighter, and presents frame bounds for fractionally dilated 2D wavelets. The results show that the frame bounds for 2D Gabor wavelets depend on the sampling density, the number of sampling orientations, and the frequency steps per octave. The paper concludes that the frame bounds for 2D Gabor wavelets are determined by these parameters and that the wavelets can form a frame under certain conditions. The results are supported by image reconstruction experiments and theoretical analysis.