In essence, no - the diffraction limit cannot be overcome if we want to create a natural color picture.
Even with an optically perfect lens, the resolution will always be limited by the Rayleigh Criterion as pointed out by the other answers (if no other limits like e.g. lens imperfections lower our resolution further).
Common super-resolution techniques that can be used in this setting today try to increase the resolution by recovering the original image that we can only sub-sample with our limited sensor resolution (combining multiple slightly offset images) and/or modelling the imperfections of the optical system that prohibit it from reaching the diffraction limit. Another concept worth mentioning here is apodization, but this does not overcome the diffraction limit but merely removes the non-central maxima of the Airy disk.
The resolution limits are extensively studied in microscopy, so it is useful to look at the techniques developed there.
However, all the techniques that provide resolution beyond the diffraction limit are not applicable to form a natural color image and without altering the subject.
Example
For an Airy disk that results from a given aperture we can take a look at two extreme cases. In the first case we have a sensor that has a significantly higher pixel pitch than the size of the Airy disk. In this case, we severely subsample the Airy disk and only know that there is a blob of light hitting a single pixel, but nothing about its shape.
But we can get more information on the shape if we create multiple exposures that are ever-so-slightly shifted, effectively creating virtual pixels of a smaller size when we apply our math appropriately.
(Sidenote: With today's fill factors of close to 100%, the achievable resolution increase is limited. This is because we are sampling with a rectangular window instead of a single point, but I couldn't find an authoritative source on the limit and have not worked out the math myself yet.)
See below - Theoretical foundations
However consider now that we massively increase our sensor resolution (or we can pixel shift to that effect), then we sample the Airy disk so much that we can perfectly reconstruct its true shape. We cannot know however what the true shape of the object is that creates the Airy disk - it might be as small as a single nanometer-sized photon source, but its image is never smaller than this blob of a few microns in diameter.
As far as I know for color images, the only possible improvement to the size of the Airy disk for a given lens aperture is in the special situation of transparencies - the size of the airy disk can be decreased by a factor of 2 with a special lighting setup and a bit further using oil immersion - these techniques alter the numerical aperture of the whole optical system, instead of just the lenses'.
See also this question for more insights on the diffraction limit.
Theoretical foundations
(Note: This section still needs work in terms of readability and clarity, but it also has become mostly redundant after finding the wikipedia article on optical resolution)
The topic can be approached from a spectral perspective. Let's initially consider the 1D case.
Every point in the photographed scene can be understood as a single dirac pulse - from a spectral view, a dirac pulse contains all frequencies equally (|F(f)| =const.).
Passing this through an ideal but finite-sized aperture leads to a low pass filtering - the single point gets projected onto a less sharp image we call the Airy disk - whose frequency spectrum follows the square of the sinc function (|F(f)| ∝ sinc^2(f)). So this attenuation of higher frequencies is the fundamental limit in resolution for a given optical setup.
Now add the sensor to the equation.
The sensor consists of many rectangular photosites and for modern image sensors let's assume pixel pitch equals pixel size (e. g. 100% fill factor). Each of these squares has a frequency spectrum of $$|F(f)| = \frac{1}{X_{pixel}}sinc(f \frac{1}{X_{pixel}})|$$. However, the spatial frequencies that lie beyond half the pixel pitch will become visible as aliasing in the image - to avoid this, we need to shrink the distance of the pixels down to the point that sufficiently much of the information in the airy spectrum remains in the pass-band of the sensor. Practically however we cannot create pixel pitches smaller than the pixels themselves. They cannot overlap, right?
Enter pixel-shift. With pixel shift, even though the individual pixels stay the same and each individual pixels spectral transform remains the same, taking multiple shots of the same subject with sub-pixel shifts allows us to decrease the pixel pitch beyond the physical distances. If we halve the pixel pitch, we double the aliasing frequency.
If this thought process is correct, we can infer:
- The size of the airy disk limits is a fundamental limit to the resolution.
The smaller the airy disk, the more frequencies remain.
- The size of the photosites limits the amount of information the sensor can capture.
The smaller the photosites, the higher the frequencies that each of these can resolve.
- The pixel pitch determines the frequency at which aliasing artifacts occur and therefore how much the aliased content degrades our image.
The smaller the pixel pitch, the higher the aliasing/Nyquist frequency. The pixel pitch can be decreased by pixel shifting.
So in essence, the frequencies present behind the lens are the result of the interplay between the airy disk given as a function of f-number, the photosite size and the pixel pitch $$ sinc^2(\mathrm{aperture}), sinc(X_{\mathrm{pixel}}), DiracComb(X_{\mathrm{pitch}})$$.
Considering the 2D case, we also have to look at the shape of the aperture and the pixels - to say it in the words of Sepp Herberger: "The round must go in the square", where the round is the image of the aperture - the airy disk and the square is the pixels. But for the moment, this will be left as an excercise for the reader ;)