how to correct scan mistakes, like rotation?

I'm trying to scan some pictures together (personal 3x4 cm images) and then split them into separated images. the first step about scanning is done but about second step (edge detection and splitting) I've some problems.
1- Normally when they scan pictures, some pictures rotate some degrees and its preventing me to have straight edges.
2- How do I remove big noises? (Imagine when they scan those pictures, they put a paper behind them. sometimes the paper makes some edges in the scanned picture... how can I understand that its not the edge I'm looking for?)

Here is a sample image:

Solution

The sample images within the scan are all rectangular, and they are all roughly the same size. There are a variety of techniques for finding rectangles in an image (even at completely arbitrary rotation), but I'll start with the more fundamental techniques.

Hough line fit can be used to find lines in an image, even when the background is noisy. From the Hough line fits you can find intersection points and perhaps compare those intersection points to points found with corner detections (see 3 below).
Edge points on lines have gradients perpendicular to those lines. When searching for edge points, you can favor edge points that are roughly a distance L or a distance W from other edge points with gradients in the parallel direction, where L and W are the known length and width of your images.
Corner detectors can help identify corners of your small rectangular images. You know the length and width of the pictures, which should help you accept/reject corners.
If you want to get fancy (which I don't recommend), then a simple normalized cross-correlation technique could detect all instances of a "template" subimage within a larger image. The technique is a bit crude, but it works okay if there isn't much rotation. Since the subimages have well-defined borders of known shape and (presumably) consistent size, it'd be easier just to find the edges rather than try to match the image content.

Once you've identified the location and orientation of each rectangular subimage, then a simple rotational transform + interpolation could generate a "right side up" version of each image. With scanners you won't have problems with perspective distortion, but if at some point in the future you would take pictures of pictures (?) at an angle, then an affine transform can map the distorted, trapezoidal images to rectangular images.

Hough transform http://en.wikipedia.org/wiki/Hough_transform

Corner detection http://en.wikipedia.org/wiki/Corner_detection

For simple edge detection that should work sufficiently well for your application, see the section "Other first-order methods" in the Edge Detection article on Wikipedia. The technique is easy to understand and simple to implement. http://en.wikipedia.org/wiki/Edge_detection

Good luck, and once again Happy New Year!