In practice

images of high resolution are necessary and preferred.The most direct solution to increase resolu- tion is to improve that of imaging sensor.However

the solution may not be feasible due to the growing cost and limitations on current image sensor and optics manufacturing technology. In recent years

many attentions have been attached on a technique called"super-resolution（SR）"which provides an alternative for increasing the resolution of the acquired images.The super-resolution reconstruction problem refers to restoring a high resolution image from multiple low resolution images degraded by warping

blurring

noise and aliasing. The core idea of SR is that the observed low resolution images contain slightly different views of the same object.In this case

the requirement of total information about the object is much higher than information in each frame.If the object doesn’t move and is identical in all frames

no extra information can be extracted from the low resolution images. The SR algorithm can be divided by their domain:frequency and spatial.Even since Tsai and Huang（1984）dem- onstrated how to get super-resolution in frequency domain

much work has been devoted to this problem.Although the fre- quency domain methods are intuitively simple and computationally cheap

they are sensitive to model errors

and only pure translational motion can be treated

so current researches are mostly concentrated on spatial domain in which allows more complicated motion models and prior knowledge can be taken into account to improve the performance of reconstruction. To analyze the SR reconstruction problem comprehensively

it’s necessary to formulate an observation model that re- lates the original high resolution image to the observed low resolution images first.Several observation models have been proposed in previous literatures.According to existing models

SR reconstruction is related to motion estimation

image restoration and interpolation.There are relative motions among each observed images

and we have to estimate the motions to align each low resolution image to a reference image before we can accumulate information from the observed images. After that

image restoration should be taken because the low resolution images are blurred in the formation model.It’s an ill-pose inverse problem which doesn’t have direct solution and usually requires regularization（applying some constraints according to prior knowledge）. Because SR reconstruction can overcome the limitations of imaging system to improve image resolution under some conditions

it’s become more attractive

especially for the situations in which it’s easy to capture multiple low resolution images.Remote sensing imaging is one of such situations.Along with the speedup of remote sensing technology

it’s much convenient to get multiple images of the same place

but these images usually are not satisfied for our need of high resolu- tion.In this paper

we are trying to reveal more details from the observed low resolution images. The image formation model is introduced first.And then a motion estimation algorithm based on 6-parameters affine transformation is proposed.Finally constrained least squares method is chosen to regularize this ill-posed inverse problem. Practically acquired remote sensing images are used in the experiment

and three criterions are selected to evaluate the quality of restored image

which demonstrate the efficiency and practicability of the algorithm.