With scipy.convolve2d(), we can sharpen an RGB image as well. We have to apply the convolution separately for each image channel.

Let's use the tajmahal.jpg image, with the emboss kernel and the schar edge detection complex kernel:

im = misc.imread('../images/tajmahal.jpg')/255 # scale each pixel value in [0,1]
print(np.max(im))
print(im.shape)
emboss_kernel = np.array([[-2,-1,0],[-1,1,1],[0,1,2]])
edge_schar_kernel = np.array([[ -3-3j, 0-10j, +3 -3j], [-10+0j, 0+ 0j, +10+0j], [ -3+3j, 0+10j, +3 +3j]])
im_embossed = np.ones(im.shape)
im_edges = np.ones(im.shape)
for i in range(3):
    im_embossed[...,i] = np.clip(signal.convolve2d(im[...,i], emboss_kernel, mode='same', boundary="symm"),0,1)
for i in range(3):
    im_edges[:,:,i] = np.clip(np.real(signal.convolve2d(im[...,i], edge_schar_kernel, mode='same', boundary="symm")),0,1)
fig, axes = pylab.subplots(nrows=3, figsize=(20, 30))
axes[0].imshow(im)
axes[0].set_title('Original Image', size=20)
axes[1].imshow(im_embossed)
axes[1].set_title('Embossed Image', size=20)
axes[2].imshow(im_edges)
axes[2].set_title('Schar Edge Detection', size=20)
for ax in axes:
    ax.axis('off')
pylab.show()

You'll get your original image along with the convolved images with a couple of different kernels:

The embossed image is as follows:

The image with schar edge detection is as follows: